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add gensim code

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  1. .gitignore +155 -0
  2. BLOG.md +132 -0
  3. README.md +2 -1
  4. app.py +47 -74
  5. cliport/__init__.py +7 -0
  6. cliport/agents/__init__.py +79 -0
  7. cliport/agents/transporter.py +539 -0
  8. cliport/agents/transporter_image_goal.py +161 -0
  9. cliport/agents/transporter_lang_goal.py +386 -0
  10. cliport/cfg/config.yaml +30 -0
  11. cliport/cfg/data.yaml +34 -0
  12. cliport/cfg/eval.yaml +50 -0
  13. cliport/cfg/train.yaml +59 -0
  14. cliport/dataset.py +940 -0
  15. cliport/demos.py +117 -0
  16. cliport/demos_gpt4.py +357 -0
  17. cliport/environments/__init__.py +0 -0
  18. cliport/environments/assets/bags/bl_sphere_bag_basic_000.mtl +12 -0
  19. cliport/environments/assets/bags/bl_sphere_bag_basic_000.obj +1587 -0
  20. cliport/environments/assets/bags/bl_sphere_bag_basic_001.mtl +12 -0
  21. cliport/environments/assets/bags/bl_sphere_bag_basic_001.obj +1458 -0
  22. cliport/environments/assets/bags/bl_sphere_bag_basic_002.mtl +12 -0
  23. cliport/environments/assets/bags/bl_sphere_bag_basic_002.obj +1329 -0
  24. cliport/environments/assets/bags/bl_sphere_bag_basic_003.mtl +12 -0
  25. cliport/environments/assets/bags/bl_sphere_bag_basic_003.obj +1200 -0
  26. cliport/environments/assets/bags/bl_sphere_bag_basic_004.mtl +12 -0
  27. cliport/environments/assets/bags/bl_sphere_bag_basic_004.obj +1071 -0
  28. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.1_numV_257.mtl +10 -0
  29. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.1_numV_257.obj +1071 -0
  30. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.1_numV_257_top_ring.txt +32 -0
  31. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.3_numV_289.mtl +10 -0
  32. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.3_numV_289.obj +1200 -0
  33. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.3_numV_289_top_ring.txt +32 -0
  34. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321.mtl +10 -0
  35. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321.obj +1329 -0
  36. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.4_numV_321_top_ring.txt +32 -0
  37. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.6_numV_353.mtl +10 -0
  38. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.6_numV_353.obj +1458 -0
  39. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.6_numV_353_top_ring.txt +32 -0
  40. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385.mtl +10 -0
  41. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385.obj +1587 -0
  42. cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385_top_ring.txt +32 -0
  43. cliport/environments/assets/ball/ball-template.urdf +31 -0
  44. cliport/environments/assets/ball/ball-template_COLOR.urdf +31 -0
  45. cliport/environments/assets/ball/ball-template_DIM.urdf +31 -0
  46. cliport/environments/assets/ball/ball.urdf +32 -0
  47. cliport/environments/assets/block/block.urdf +31 -0
  48. cliport/environments/assets/block/block_for_anchors.urdf +32 -0
  49. cliport/environments/assets/block/small.urdf +31 -0
  50. cliport/environments/assets/bowl/bowl.urdf +29 -0
.gitignore ADDED
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+ # Folders
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+ cliport/data/
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+ cliport/outputs/
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+ cliport/notebooks/
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+ cliport/jobs/
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+ jobs/
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+ videos/
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+ checkpoints/
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+
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+ # Large Asserts
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+ cliport/environments/assets/google/
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+
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+ # Idea
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+ .idea
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+
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ pip-wheel-metadata/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
43
+ MANIFEST
44
+ wandb
45
+ # PyInstaller
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+ # Usually these files are written by a python script from a template
47
+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
49
+ *.spec
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+
51
+ # Installer logs
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+ pip-log.txt
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+ pip-delete-this-directory.txt
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+
55
+ # Unit test / coverage reports
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+ htmlcov/
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+ .tox/
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+ .nox/
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+ .coverage
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+ .coverage.*
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+ .cache
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+ nosetests.xml
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+ coverage.xml
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+ *.cover
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+ *.py,cover
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+ .hypothesis/
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+ .pytest_cache/
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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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+ # Django stuff:
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+
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+ # Scrapy stuff:
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+ .scrapy
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+
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+ # Sphinx documentation
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+ docs/_build/
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+
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+ # PyBuilder
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+ target/
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+
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+
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+ # IPython
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+ profile_default/
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+ ipython_config.py
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+
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+ # pyenv
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+ .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
104
+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
105
+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow
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+ __pypackages__/
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+
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+ # Celery stuff
113
+ celerybeat-schedule
114
+ celerybeat.pid
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+
116
+ # SageMath parsed files
117
+ *.sage.py
118
+
119
+ # Environments
120
+ .env
121
+ .venv
122
+ venv/
123
+ ENV/
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+
125
+ # Spyder project settings
126
+ .spyderproject
127
+ .spyproject
128
+
129
+ # Rope project settings
130
+ .ropeproject
131
+
132
+ # mkdocs documentation
133
+ /site
134
+
135
+ # mypy
136
+ .mypy_cache/
137
+ .dmypy.json
138
+ dmypy.json
139
+
140
+ # Pyre type checker
141
+ .pyre/
142
+
143
+
144
+
145
+ output
146
+ data
147
+ .hydra
148
+ cliport/environments/assets_backup
149
+ 0_VRDemoSettings.txt
150
+ demo*.log
151
+ outputs
152
+ *.DS_Store
153
+
154
+ multirun
155
+ exps-singletask/
BLOG.md ADDED
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1
+ # Supersizing Simulation Task Generation in Robotics with LLM
2
+
3
+
4
+ ## Overview
5
+ Collecting real-world interaction data to train general robotic policies is prohibitively expensive, thus motivating the use of simulation data. Despite abundant single-task simulation data in terms of object instances and poses among others, the task-level diversity in simulation have remained a challenge. On the other hand, the breakthrough in language domain, such as GPT-4, has shown impressive coding skills and natural language understanding capability, but its usage in robotics has been mostly on policy execution, planning, and log summary. This repository explores the use of a LLM code generation pipeline to generate simulation environments and expert demonstrations for diverse simulation tasks. In particular, the task-level diversity is crucial for general-purpose manipulation policy learning. This simulation task generation pipeline can be top-down: given a target task, it proposes a task curriculum to iteratively approach the complexity of the target task; the pipeline can also work in a bottom-up manner: it bootstraps from previous tasks and iteratively proposes more interesting tasks, and these task code can be used to generate demonstrations to train a policy.
6
+
7
+ We develop an LLM pipeline for generating simulation environments and tasks through program synthesis, as data augmentations for robotic policy learning. The framework consists of three novel components:
8
+ 1. an automated prompting mechanism that proposes new tasks and implementations for open-world task design
9
+ 2. a task library for developing more complex simulation environments and data generations
10
+ 3. a GPT-4 generated incremental benchmark and a language-conditioned multi-task policy training method that leverages the large set of generated tasks, and close the loop on evaluating the task generation pipeline.
11
+
12
+
13
+ Note: Although the field has different opinions on what tasks and skills are, in this work we consider each simulation code defines a task. Therefore the [Ravens](https://github.com/google-research/ravens/tree/master) benchmark has 10 tasks in total.
14
+
15
+
16
+ ![](media/zoom_task.gif)
17
+
18
+
19
+
20
+ ## Prompt Recipe
21
+ Although we can prompt GPT-4 directly to generate simulation environment code for training manipulation policies, it lacks the contexts and the capability required to build an increasing task benchmark. We formulate the task and program synthesis problems into an agent prompting mechanism with a task design agent and a task library (or memory). These sub-components are all powered by few-shot and chain-of-thoughts prompts on large language models that have distilled internet-scale knowledge and offer the reasoning and exploration capability necessary for simulation task generations.
22
+
23
+
24
+ We have developed both top-down and bottom-up task approaches in our method. The top-down approach takes a desired task as a prompt and gradually generates more complex tasks to achieve this target task. This is helpful if the user apriori has a desired task or wants to design a task curriculum to build complex agents. For instance, to train a policy to accomplish long-horizon tasks such as build-house, we can ask LLM to generate more basic tasks like building a base or building a roof. The bottom-up approach shows the LLM the previous tasks that have been designed, directly requests for a new task, and further bootstraps itself iteratively. The goal is then to generate as diverse and interesting tasks as possible for downstream policy training. The details of the prompt is shown in the figure and section below.
25
+
26
+ ![](media/prompting_pipeline.gif)
27
+
28
+ ## Task Design Prompt
29
+ The goal of the task design prompt is to propose novel task descriptions and their code implementations, which can be further broken down into scene code and demonstration code. In particular, we use the [Ravens](https://github.com/google-research/ravens/tree/master) benchmark codebase with TransporterNets that use affordance prediction to solve table-top top-down manipulation tasks. The task design can handle any motion primitive like pushing, sliding, etc. that can be parameterized by two
30
+ end-effector poses at each timestep. From the figure, a standard gym-style simulation environment code, the reset function, which is inherited from a base task class and takes in the environment as an argument, efficiently represents the assets and their attributes, poses and their relative configurations, and spatial and language goals that are used to parameterize the per-step demonstration.
31
+
32
+
33
+ To reach this level of lengthy code generation, we break the prompt of the agent into several steps to enforce its logical structure (e.g. [prompt](prompts/bottomup_task_generation_prompt/)): task description generation, API and common mistake summary, few-shot reference code selection, and code generation. The input prompt to GPT-4 task generation stage consists of several components:
34
+ 1. available assets that are in the codebase
35
+ 2. samples of reference tasks from the task library (discussed in the next section) to act as few-shot examples
36
+ 3. also provide past task names to make sure the agent does not provide overlapped tasks.
37
+ 4. some examples of bad tasks and the reasons that they are bad (for example, not physically feasible)
38
+ 5. some additional rules (such as do not use assets beyond what is available) and the output format. This stage has temperature=1 to encourage diversity and the rest components would have temperature=0 to have some robustness.
39
+
40
+
41
+ The asset generation, which generates the URDFs for loading into a scene, has not been explored much in the pipeline. The API prompt consists of some major function implementations of the base task class as well as an explanation of the `goal` variable that represents the action labels. This is important to help GPT understands some useful helper functions in the base class (such as `get_random_pose` as well as some pybullet (simulation engine) basics. The common error prompt is a list of past errors that GPT-4 has made as well as some high-level errors that are summarized, to help it avoid making repeated errors. These components are optional.
42
+
43
+
44
+ Finally, reference code selection and code reference prompt will show LLM the generated task names and ask GPT which ones are useful to read, and then show GPT the corresponding code to be used as reference code. This part is critical for LLM to know exactly how to implement a task class in Ravens (such as the logic of sample asset urdfs and build scene first, and then add a spatial goal and language goals).
45
+
46
+ ![](media/code_explanation.png)
47
+
48
+ <details><summary>
49
+ Prompt Metric Ablation
50
+ </summary>
51
+
52
+ ![](media/prompt_metric.png)
53
+
54
+ </details>
55
+
56
+ ## Task Library (Memory)
57
+ An important differentiator of an agent-based LLM pipeline is that it has a memory of its past actions. In this case, our agent is a simulation task and code programmer and its environment is the physics simulation and human users. The task library has a few roles, for one it provides what past tasks are (in the task generation stage) and past codes are (in the code generation stage) to the task design agent such that it will not try to overlap tasks. It also acts as a benchmark to accumulate all past tasks and bootstrap for more novel tasks. Its saved task codes can be run offline to generate demonstration data. We can also visualize the tasks with an embedding space. The memory also contains a key component: the critic that reflects on the task and code that the agent designed and the reference code, and decides whether the new code will be added to the task memory. The task reflection stage prompt has the following component
58
+ 1. the generated task description and code
59
+ 2. the current tasks in the library
60
+ 3. some examples of accepting and rejecting new task, and then LLM is prompted to answer whether to accept this new task and this improvement will also be in the context window in the next round of the agent task design.
61
+
62
+
63
+ Note that to improve the robustness of this stage, we prompt GPT three times in parallel to get diverse answers with temperature 0.5, and only accept the task if there is an agreement. We show some selected generated tasks by GPT to explore different reasoning capability of GPT-4.
64
+
65
+ <details><summary>
66
+ Stack-Tasks Examples (Complexity)
67
+ </summary>
68
+
69
+ ![](media/stack_task.gif)
70
+
71
+ </details>
72
+
73
+ <details><summary>
74
+ Build-Task Examples (Creativity)
75
+ </summary>
76
+
77
+ ![](media/build_task.gif)
78
+
79
+ </details>
80
+
81
+ <details><summary>
82
+ Pick-and-Place-Task Examples (Compositionality)
83
+ </summary>
84
+
85
+ ![](media/pick_place_task.gif)
86
+
87
+ </details>
88
+ <!-- ![](media/generated_task.gif) -->
89
+
90
+ ## Policy Training
91
+ Once the tasks are generated, we can use these task codes to generate demonstration data and train manipulation policies. We use similar two-stream architectures and transporter architectures as in [CLIPORT](https://cliport.github.io/) to parametrize the policy $\pi$. The model first (i) attends to a local region to decide where to pick, then (ii) computes
92
+ a placement location by finding the best match through cross-correlation of deep visual features. The FCNs are extended to two-pathways: semantic and spatial where the semantic stream is conditioned with language features at the bottleneck and fused with intermediate features from the spatial stream. For more details, we refer the reader to the original papers.
93
+
94
+ <!-- <details><summary>
95
+ Policy Training Results
96
+ </summary>
97
+
98
+ ![](media/failure_case.gif)
99
+
100
+ </details> -->
101
+
102
+
103
+ ## Common Failure Cases
104
+ 0. The `common_error.txt` in the prompt folder shows some common failure cases of the generation.
105
+ 1. Since the simulation task code (reset function and class definition) is lengthy compared to simple function completions. It can be prone to bugs such as accessing missing functions or assets which cause compilation errors.
106
+ 2. When the tasks can be run, it could still have runtime errors such as in dynamics and geometry issues. For instance it can generate a huge object or generate task such as `balance a block on a rope` which is not grounded well.
107
+ 3. When the task has no runtime issues, the experts (represented by the `goal`) might not complete the task. Or the language descriptions can be too ambiguous to train an agent and require manual filtering.
108
+ 4. Some tasks are not zero-shot generated: such as `build-house`, `build-car`, and `manipulating-two-ropes` etc. Some tasks are mostly coded by the author for bootstrapping purpose such as `push-piles-into-letter` and `connect-boxes-with-rope`.
109
+
110
+ <details><summary>
111
+ Failure Cases
112
+ </summary>
113
+
114
+ ![](media/failure_case.gif)
115
+
116
+ </details>
117
+
118
+
119
+ ## Related Works
120
+ LLM has shown impressive potential to explore the environments and reflect upon its own actions, similar to an agent such as in [Voyager](https://voyager.minedojo.org/). Recent works have explored domain randomizations, [parametric task generations](https://sites.google.com/view/active-task-randomization), and procedural asset generations and text to 3D such as [Shape-E](https://github.com/openai/shap-e) and [Point-E](https://openai.com/research/point-e). Moreover, large language models have been applied to policy learning such as in [PALM-e](https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html) and [Say-Can](https://say-can.github.io/), task and motion planning such as in [Inner Monologue](https://innermonologue.github.io/), synthesizing policy programs such as in [Code as Policies](https://code-as-policies.github.io/) and [Language as Rewards](https://language-to-reward.github.io/). Past work has also explored LLM's physical grounded capability such as in [Mine's Eye](https://arxiv.org/abs/2210.05359).
121
+
122
+ ## Conclusion and Future Directions
123
+ Overall we explored the use of LLM in simulation environment and task generation. It has shown impressive capability to write manipulation tasks along with expert demonstrations and yet still has several drawbacks and thus future directions.
124
+ There are a few limitations which could be interesting future directions
125
+ 1. The asset diversity limits how GPT-4 can generate high diverse and creative tasks. One interesting future direction is to explore asset generation jointly with code generation.
126
+ 2. It would be cool to generate thousands of tasks using this pipeline by bootstrapping as well as train an agent that can fit these number of tasks.
127
+ 3. It would be interesting to carefully study task-level generalization. We have generated a TSNE plot of the task code embeddings by use GPT embedding AI to encode the generated code for each task below.
128
+
129
+ ![](media/task_embedding.png)
130
+
131
+ ## Acknowledgement
132
+ I would like to acknowledge [Bailin Wang](https://berlino.github.io/), [Mohit Shridhar](https://mohitshridhar.com/), and [Yoon Kim](https://people.csail.mit.edu/yoonkim/) for the helpful discussions and collaborations.
README.md CHANGED
@@ -22,10 +22,11 @@ Below is an interactive demo for the simulated tabletop manipulation domain, see
22
  1. Obtain an [OpenAI API Key](https://openai.com/blog/openai-api/)
23
 
24
  ## Usage
 
25
 
26
- ## Example Instructions
27
 
28
  ## Known Limitations
 
29
 
30
  ## Acknowledgemetn
31
  Thanks to Jacky's [code-as-policies](https://huggingface.co/spaces/jackyliang42/code-as-policies/tree/main) demo.
 
22
  1. Obtain an [OpenAI API Key](https://openai.com/blog/openai-api/)
23
 
24
  ## Usage
25
+ 1. Type in desired task name in the box.
26
 
 
27
 
28
  ## Known Limitations
29
+ 1. The code generation can fail or generate infeasible tasks.
30
 
31
  ## Acknowledgemetn
32
  Thanks to Jacky's [code-as-policies](https://huggingface.co/spaces/jackyliang42/code-as-policies/tree/main) demo.
app.py CHANGED
@@ -14,83 +14,62 @@ from sim import PickPlaceEnv, LMP_wrapper
14
  from consts import ALL_BLOCKS, ALL_BOWLS
15
  from md_logger import MarkdownLogger
16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
 
18
  class DemoRunner:
19
 
20
  def __init__(self):
21
- self._cfg = OmegaConf.to_container(OmegaConf.load('cfg.yaml'), resolve=True)
22
  self._env = None
23
- self._md_logger = MarkdownLogger()
24
-
25
- def make_LMP(self, env):
26
- # LMP env wrapper
27
- cfg = copy.deepcopy(self._cfg)
28
- cfg['env'] = {
29
- 'init_objs': list(env.obj_name_to_id.keys()),
30
- 'coords': cfg['tabletop_coords']
31
- }
32
-
33
- LMP_env = LMP_wrapper(env, cfg)
34
- # creating APIs that the LMPs can interact with
35
- fixed_vars = {
36
- 'np': np
37
- }
38
- fixed_vars.update({
39
- name: eval(name)
40
- for name in shapely.geometry.__all__ + shapely.affinity.__all__
41
- })
42
- variable_vars = {
43
- k: getattr(LMP_env, k)
44
- for k in [
45
- 'get_bbox', 'get_obj_pos', 'get_color', 'is_obj_visible', 'denormalize_xy',
46
- 'put_first_on_second', 'get_obj_names',
47
- 'get_corner_name', 'get_side_name',
48
- ]
49
- }
50
- variable_vars['say'] = lambda msg: self._md_logger.log_text(f'Robot says: "{msg}"')
51
-
52
- # creating the function-generating LMP
53
- lmp_fgen = LMPFGen(cfg['lmps']['fgen'], fixed_vars, variable_vars, self._md_logger)
54
-
55
- # creating other low-level LMPs
56
- variable_vars.update({
57
- k: LMP(k, cfg['lmps'][k], lmp_fgen, fixed_vars, variable_vars, self._md_logger)
58
- for k in ['parse_obj_name', 'parse_position', 'parse_question', 'transform_shape_pts']
59
- })
60
-
61
- # creating the LMP that deals w/ high-level language commands
62
- lmp_tabletop_ui = LMP(
63
- 'tabletop_ui', cfg['lmps']['tabletop_ui'], lmp_fgen, fixed_vars, variable_vars, self._md_logger
64
- )
65
-
66
- return lmp_tabletop_ui
67
 
68
- def setup(self, api_key, n_blocks, n_bowls):
69
  openai.api_key = api_key
70
-
71
- self._env = PickPlaceEnv(render=True, high_res=True, high_frame_rate=False)
72
- list_idxs = np.random.choice(len(ALL_BLOCKS), size=max(n_blocks, n_bowls), replace=False)
73
- block_list = [ALL_BLOCKS[i] for i in list_idxs[:n_blocks]]
74
- bowl_list = [ALL_BOWLS[i] for i in list_idxs[:n_bowls]]
75
- obj_list = block_list + bowl_list
76
- self._env.reset(obj_list)
77
-
78
- self._lmp_tabletop_ui = self.make_LMP(self._env)
79
-
80
- info = '### Available Objects: \n- ' + '\n- '.join(obj_list)
81
- img = self._env.get_camera_image()
 
82
 
83
  return info, img
84
 
85
  def run(self, instruction):
86
- if self._env is None:
87
- return 'Please run setup first!', None, None
88
 
89
  self._env.cache_video = []
90
  self._md_logger.clear()
91
 
92
  try:
93
- self._lmp_tabletop_ui(instruction, f'objects = {self._env.object_list}')
 
94
  except Exception as e:
95
  return f'Error: {e}', None, None
96
 
@@ -100,19 +79,16 @@ class DemoRunner:
100
  video_file_name = NamedTemporaryFile(suffix='.mp4').name
101
  rendered_clip.write_videofile(video_file_name, fps=25)
102
 
103
- return self._md_logger.get_log(), self._env.get_camera_image(), video_file_name
104
 
105
 
106
- def setup(api_key, n_blocks, n_bowls):
107
  if not api_key:
108
  return 'Please enter your OpenAI API key!', None, None
109
 
110
- if n_blocks + n_bowls == 0:
111
- return 'Please select at least one object!', None, None
112
-
113
  demo_runner = DemoRunner()
114
 
115
- info, img = demo_runner.setup(api_key, n_blocks, n_bowls)
116
  return info, img, demo_runner
117
 
118
 
@@ -137,10 +113,7 @@ if __name__ == '__main__':
137
  with gr.Column():
138
  with gr.Row():
139
  inp_api_key = gr.Textbox(label='OpenAI API Key (this is not stored anywhere)', lines=1)
140
- with gr.Row():
141
- inp_n_blocks = gr.Slider(label='Number of Blocks', minimum=0, maximum=4, value=3, step=1)
142
- inp_n_bowls = gr.Slider(label='Number of Bowls', minimum=0, maximum=4, value=3, step=1)
143
-
144
  btn_setup = gr.Button("Setup/Reset Simulation")
145
  info_setup = gr.Markdown(label='Setup Info')
146
  with gr.Column():
@@ -149,7 +122,7 @@ if __name__ == '__main__':
149
  with gr.Row():
150
  with gr.Column():
151
 
152
- inp_instruction = gr.Textbox(label='Instruction', lines=1)
153
  btn_run = gr.Button("Run (this may take 30+ seconds)")
154
  info_run = gr.Markdown(label='Generated Code')
155
  with gr.Column():
@@ -157,7 +130,7 @@ if __name__ == '__main__':
157
 
158
  btn_setup.click(
159
  setup,
160
- inputs=[inp_api_key, inp_n_blocks, inp_n_bowls],
161
  outputs=[info_setup, img_setup, state]
162
  )
163
  btn_run.click(
@@ -166,4 +139,4 @@ if __name__ == '__main__':
166
  outputs=[info_run, img_setup, video_run]
167
  )
168
 
169
- demo.launch()
 
14
  from consts import ALL_BLOCKS, ALL_BOWLS
15
  from md_logger import MarkdownLogger
16
 
17
+ import numpy as np
18
+ import os
19
+ import hydra
20
+ import random
21
+
22
+ import re
23
+ import openai
24
+ import IPython
25
+ import time
26
+ import pybullet as p
27
+ import traceback
28
+ from datetime import datetime
29
+ from pprint import pprint
30
+ import cv2
31
+ import re
32
+ import random
33
+ import json
34
+
35
+ from gensim.agent import Agent
36
+ from gensim.critic import Critic
37
+ from gensim.sim_runner import SimulationRunner
38
+ from gensim.memory import Memory
39
+ from gensim.utils import set_gpt_model, clear_messages
40
 
41
  class DemoRunner:
42
 
43
  def __init__(self):
 
44
  self._env = None
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45
 
46
+ def setup(self, api_key):
47
  openai.api_key = api_key
48
+ cfg['model_output_dir'] = 'temp'
49
+ cfg['prompt_folder'] = 'topdown_task_generation_prompt_simple_singleprompt'
50
+ set_gpt_model(cfg['gpt_model'])
51
+ cfg['load_memory'] = True
52
+ cfg['task_description_candidate_num'] = 10
53
+ cfg['record']['save_video'] = True
54
+ memory = Memory(cfg)
55
+ agent = Agent(cfg, memory)
56
+ critic = Critic(cfg, memory)
57
+ self.simulation_runner = SimulationRunner(cfg, agent, critic, memory)
58
+
59
+ info = '### Build'
60
+ img = np.zeros((720, 640, 0))
61
 
62
  return info, img
63
 
64
  def run(self, instruction):
65
+ cfg['target_task_name'] = instruction
 
66
 
67
  self._env.cache_video = []
68
  self._md_logger.clear()
69
 
70
  try:
71
+ self.simulation_runner.task_creation()
72
+ self.simulation_runner.simulate_task()
73
  except Exception as e:
74
  return f'Error: {e}', None, None
75
 
 
79
  video_file_name = NamedTemporaryFile(suffix='.mp4').name
80
  rendered_clip.write_videofile(video_file_name, fps=25)
81
 
82
+ return self.simulation_runner.chat_log, self.simulation_runner.env.curr_video, video_file_name
83
 
84
 
85
+ def setup(api_key):
86
  if not api_key:
87
  return 'Please enter your OpenAI API key!', None, None
88
 
 
 
 
89
  demo_runner = DemoRunner()
90
 
91
+ info, img = demo_runner.setup(api_key)
92
  return info, img, demo_runner
93
 
94
 
 
113
  with gr.Column():
114
  with gr.Row():
115
  inp_api_key = gr.Textbox(label='OpenAI API Key (this is not stored anywhere)', lines=1)
116
+
 
 
 
117
  btn_setup = gr.Button("Setup/Reset Simulation")
118
  info_setup = gr.Markdown(label='Setup Info')
119
  with gr.Column():
 
122
  with gr.Row():
123
  with gr.Column():
124
 
125
+ inp_instruction = gr.Textbox(label='Task Name', lines=1)
126
  btn_run = gr.Button("Run (this may take 30+ seconds)")
127
  info_run = gr.Markdown(label='Generated Code')
128
  with gr.Column():
 
130
 
131
  btn_setup.click(
132
  setup,
133
+ inputs=[inp_api_key],
134
  outputs=[info_setup, img_setup, state]
135
  )
136
  btn_run.click(
 
139
  outputs=[info_run, img_setup, video_run]
140
  )
141
 
142
+ demo.launch(debug=True)
cliport/__init__.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ """Package init."""
2
+
3
+ from cliport import agents
4
+ from cliport import models
5
+ from cliport import tasks
6
+ from cliport.dataset import RavensDataset
7
+ from cliport.environments.environment import Environment
cliport/agents/__init__.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from cliport.agents.transporter import OriginalTransporterAgent
2
+ from cliport.agents.transporter import ClipUNetTransporterAgent
3
+ from cliport.agents.transporter import TwoStreamClipWithoutSkipsTransporterAgent
4
+ from cliport.agents.transporter import TwoStreamRN50BertUNetTransporterAgent
5
+ from cliport.agents.transporter import TwoStreamClipUNetTransporterAgent
6
+
7
+ from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetTransporterAgent
8
+ from cliport.agents.transporter_lang_goal import TwoStreamRN50BertLingUNetTransporterAgent
9
+ from cliport.agents.transporter_lang_goal import TwoStreamUntrainedRN50BertLingUNetTransporterAgent
10
+ from cliport.agents.transporter_lang_goal import OriginalTransporterLangFusionAgent
11
+ from cliport.agents.transporter_lang_goal import ClipLingUNetTransporterAgent
12
+ from cliport.agents.transporter_lang_goal import TwoStreamRN50BertLingUNetLatTransporterAgent
13
+
14
+ from cliport.agents.transporter_image_goal import ImageGoalTransporterAgent
15
+
16
+ from cliport.agents.transporter import TwoStreamClipUNetLatTransporterAgent
17
+ from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetLatTransporterAgent
18
+ from cliport.agents.transporter_lang_goal import TwoStreamClipFilmLingUNetLatTransporterAgent
19
+ from cliport.agents.transporter_lang_goal import TwoStreamClipFilmLingUNetLatTransporterAgent, TwoStreamClipLingUNetLatTransporterAgentReduce, TwoStreamClipLingUNetLatTransporterAgentReducePretrained
20
+ from cliport.agents.transporter_lang_goal import TwoStreamClipLingUNetLatTransporterAgentReduceOneStream
21
+
22
+ names = {
23
+ ################################
24
+ ### CLIPort ###
25
+ 'cliport': TwoStreamClipLingUNetLatTransporterAgent,
26
+ 'cliport_reduce': TwoStreamClipLingUNetLatTransporterAgentReduce,
27
+ 'cliport_reduce_pretrain': TwoStreamClipLingUNetLatTransporterAgentReducePretrained,
28
+ 'cliport_reduce_onestream': TwoStreamClipLingUNetLatTransporterAgentReduceOneStream,
29
+ 'two_stream_clip_lingunet_lat_transporter': TwoStreamClipLingUNetLatTransporterAgent,
30
+
31
+ ################################
32
+ ### Two-Stream Architectures ###
33
+ # CLIPort without language
34
+ 'two_stream_clip_unet_lat_transporter': TwoStreamClipUNetLatTransporterAgent,
35
+
36
+ # CLIPort without lateral connections
37
+ 'two_stream_clip_lingunet_transporter': TwoStreamClipLingUNetTransporterAgent,
38
+
39
+ # CLIPort without language and lateral connections
40
+ 'two_stream_clip_unet_transporter': TwoStreamClipUNetTransporterAgent,
41
+
42
+ # CLIPort without language, lateral, or skip connections
43
+ 'two_stream_clip_woskip_transporter': TwoStreamClipWithoutSkipsTransporterAgent,
44
+
45
+ # RN50-BERT
46
+ 'two_stream_full_rn50_bert_lingunet_lat_transporter': TwoStreamRN50BertLingUNetLatTransporterAgent,
47
+
48
+ # RN50-BERT without language
49
+ 'two_stream_full_rn50_bert_unet_transporter': TwoStreamRN50BertUNetTransporterAgent,
50
+
51
+ # RN50-BERT without lateral connections
52
+ 'two_stream_full_rn50_bert_lingunet_transporter': TwoStreamRN50BertLingUNetTransporterAgent,
53
+
54
+ # Untrained RN50-BERT (similar to untrained CLIP)
55
+ 'two_stream_full_untrained_rn50_bert_lingunet_transporter': TwoStreamUntrainedRN50BertLingUNetTransporterAgent,
56
+
57
+ ###################################
58
+ ### Single-Stream Architectures ###
59
+ # Transporter-only
60
+ 'transporter': OriginalTransporterAgent,
61
+
62
+ # CLIP-only without language
63
+ 'clip_unet_transporter': ClipUNetTransporterAgent,
64
+
65
+ # CLIP-only
66
+ 'clip_lingunet_transporter': ClipLingUNetTransporterAgent,
67
+
68
+ # Transporter with language (at bottleneck)
69
+ 'transporter_lang': OriginalTransporterLangFusionAgent,
70
+
71
+ # Image-Goal Transporter
72
+ 'image_goal_transporter': ImageGoalTransporterAgent,
73
+
74
+ ##############################################
75
+ ### New variants NOT reported in the paper ###
76
+
77
+ # CLIPort with FiLM language fusion
78
+ 'two_stream_clip_film_lingunet_lat_transporter': TwoStreamClipFilmLingUNetLatTransporterAgent,
79
+ }
cliport/agents/transporter.py ADDED
@@ -0,0 +1,539 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import numpy as np
3
+
4
+ import torch
5
+ import torch.nn.functional as F
6
+ from pytorch_lightning import LightningModule
7
+
8
+ from cliport.tasks import cameras
9
+ from cliport.utils import utils
10
+ from cliport.models.core.attention import Attention
11
+ from cliport.models.core.transport import Transport
12
+ from cliport.models.streams.two_stream_attention import TwoStreamAttention
13
+ from cliport.models.streams.two_stream_transport import TwoStreamTransport
14
+
15
+ from cliport.models.streams.two_stream_attention import TwoStreamAttentionLat
16
+ from cliport.models.streams.two_stream_transport import TwoStreamTransportLat
17
+ import time
18
+ import IPython
19
+
20
+ class TransporterAgent(LightningModule):
21
+ def __init__(self, name, cfg, train_ds, test_ds):
22
+ super().__init__()
23
+ utils.set_seed(0)
24
+ self.automatic_optimization=False
25
+ self.device_type = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # this is bad for PL :(
26
+ self.name = name
27
+ self.cfg = cfg
28
+ self.train_loader = train_ds
29
+ self.test_loader = test_ds
30
+
31
+ self.train_ds = train_ds.dataset
32
+ self.test_ds = test_ds.dataset
33
+
34
+ self.name = name
35
+ self.task = cfg['train']['task']
36
+ self.total_steps = 0
37
+ self.crop_size = 64
38
+ self.n_rotations = cfg['train']['n_rotations']
39
+
40
+ self.pix_size = 0.003125
41
+ self.in_shape = (320, 160, 6)
42
+ self.cam_config = cameras.RealSenseD415.CONFIG
43
+ self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
44
+
45
+ self.val_repeats = cfg['train']['val_repeats']
46
+ self.save_steps = cfg['train']['save_steps']
47
+
48
+ self._build_model()
49
+ ##
50
+ # reduce the number of parameters here
51
+ ##
52
+ self._optimizers = {
53
+ 'attn': torch.optim.Adam(self.attention.parameters(), lr=self.cfg['train']['lr']),
54
+ 'trans': torch.optim.Adam(self.transport.parameters(), lr=self.cfg['train']['lr'])
55
+ }
56
+ print("Agent: {}, Logging: {}".format(name, cfg['train']['log']))
57
+
58
+ def configure_optimizers(self):
59
+ return self._optimizers
60
+
61
+ def _build_model(self):
62
+ self.attention = None
63
+ self.transport = None
64
+ raise NotImplementedError()
65
+
66
+ def forward(self, x):
67
+ raise NotImplementedError()
68
+
69
+ def cross_entropy_with_logits(self, pred, labels, reduction='mean'):
70
+ # Lucas found that both sum and mean work equally well
71
+ x = (-labels.view(len(labels), -1) * F.log_softmax(pred.view(len(labels), -1), -1))
72
+ if reduction == 'sum':
73
+ return x.sum()
74
+ elif reduction == 'mean':
75
+ return x.mean()
76
+ else:
77
+ raise NotImplementedError()
78
+
79
+ def attn_forward(self, inp, softmax=True):
80
+ inp_img = inp['inp_img']
81
+ output = self.attention.forward(inp_img, softmax=softmax)
82
+ return output
83
+
84
+ def attn_training_step(self, frame, backprop=True, compute_err=False):
85
+ inp_img = frame['img']
86
+ p0, p0_theta = frame['p0'], frame['p0_theta']
87
+
88
+ inp = {'inp_img': inp_img}
89
+ out = self.attn_forward(inp, softmax=False)
90
+ return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta)
91
+
92
+ def attn_criterion(self, backprop, compute_err, inp, out, p, theta):
93
+ # Get label.
94
+ if type(theta) is torch.Tensor:
95
+ theta = theta.detach().cpu().numpy()
96
+
97
+ theta_i = theta / (2 * np.pi / self.attention.n_rotations)
98
+ theta_i = np.int32(np.round(theta_i)) % self.attention.n_rotations
99
+ inp_img = inp['inp_img'].float()
100
+
101
+ label_size = inp_img.shape[:3] + (self.attention.n_rotations,)
102
+ label = torch.zeros(label_size, dtype=torch.float, device=out.device)
103
+
104
+ # remove this for-loop laters
105
+ for idx, p_i in enumerate(p):
106
+ label[idx, int(p_i[0]), int(p_i[1]), theta_i[idx]] = 1
107
+ label = label.permute((0, 3, 1, 2)).contiguous()
108
+
109
+ # Get loss.
110
+ loss = self.cross_entropy_with_logits(out, label)
111
+
112
+ # Backpropagate.
113
+ if backprop:
114
+ attn_optim = self._optimizers['attn']
115
+ self.manual_backward(loss)
116
+ attn_optim.step()
117
+ attn_optim.zero_grad()
118
+
119
+ # Pixel and Rotation error (not used anywhere).
120
+ err = {}
121
+ if compute_err:
122
+ with torch.no_grad():
123
+ pick_conf = self.attn_forward(inp)
124
+ pick_conf = pick_conf[0].permute(1,2,0)
125
+ pick_conf = pick_conf.detach().cpu().numpy()
126
+ p = p[0]
127
+ theta = theta[0]
128
+
129
+ # single batch
130
+ argmax = np.argmax(pick_conf)
131
+ argmax = np.unravel_index(argmax, shape=pick_conf.shape)
132
+ p0_pix = argmax[:2]
133
+ p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2])
134
+
135
+ err = {
136
+ 'dist': np.linalg.norm(np.array(p.detach().cpu().numpy()) - p0_pix, ord=1),
137
+ 'theta': np.absolute((theta - p0_theta) % np.pi)
138
+ }
139
+ return loss, err
140
+
141
+ def trans_forward(self, inp, softmax=True):
142
+ inp_img = inp['inp_img']
143
+ p0 = inp['p0']
144
+
145
+ output = self.transport.forward(inp_img, p0, softmax=softmax)
146
+ return output
147
+
148
+ def transport_training_step(self, frame, backprop=True, compute_err=False):
149
+ inp_img = frame['img'].float()
150
+ p0 = frame['p0']
151
+ p1, p1_theta = frame['p1'], frame['p1_theta']
152
+
153
+ inp = {'inp_img': inp_img, 'p0': p0}
154
+ output = self.trans_forward(inp, softmax=False)
155
+ err, loss = self.transport_criterion(backprop, compute_err, inp, output, p0, p1, p1_theta)
156
+ return loss, err
157
+
158
+ def transport_criterion(self, backprop, compute_err, inp, output, p, q, theta):
159
+ s = time.time()
160
+ if type(theta) is torch.Tensor:
161
+ theta = theta.detach().cpu().numpy()
162
+
163
+ itheta = theta / (2 * np.pi / self.transport.n_rotations)
164
+ itheta = np.int32(np.round(itheta)) % self.transport.n_rotations
165
+
166
+ # Get one-hot pixel label map.
167
+ inp_img = inp['inp_img']
168
+
169
+ # label_size = inp_img.shape[:2] + (self.transport.n_rotations,)
170
+ label_size = inp_img.shape[:3] + (self.transport.n_rotations,)
171
+ label = torch.zeros(label_size, dtype=torch.float, device=output.device)
172
+
173
+ # remove this for-loop laters
174
+ q[:,0] = torch.clamp(q[:,0], 0, label.shape[1]-1)
175
+ q[:,1] = torch.clamp(q[:,1], 0, label.shape[2]-1)
176
+
177
+ for idx, q_i in enumerate(q):
178
+ label[idx, int(q_i[0]), int(q_i[1]), itheta[idx]] = 1
179
+ label = label.permute((0, 3, 1, 2)).contiguous()
180
+
181
+ # Get loss.
182
+ loss = self.cross_entropy_with_logits(output, label)
183
+
184
+ if backprop:
185
+ transport_optim = self._optimizers['trans']
186
+ transport_optim.zero_grad()
187
+ self.manual_backward(loss)
188
+ transport_optim.step()
189
+
190
+ # Pixel and Rotation error (not used anywhere).
191
+ err = {}
192
+ if compute_err:
193
+ with torch.no_grad():
194
+ place_conf = self.trans_forward(inp)
195
+ # pick the first batch
196
+ place_conf = place_conf[0]
197
+ q = q[0]
198
+ theta = theta[0]
199
+ place_conf = place_conf.permute(1, 2, 0)
200
+ place_conf = place_conf.detach().cpu().numpy()
201
+ argmax = np.argmax(place_conf)
202
+ argmax = np.unravel_index(argmax, shape=place_conf.shape)
203
+ p1_pix = argmax[:2]
204
+ p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2])
205
+
206
+ err = {
207
+ 'dist': np.linalg.norm(np.array(q.detach().cpu().numpy()) - p1_pix, ord=1),
208
+ 'theta': np.absolute((theta - p1_theta) % np.pi)
209
+ }
210
+
211
+ self.transport.iters += 1
212
+ return err, loss
213
+
214
+ def training_step(self, batch, batch_idx):
215
+
216
+ self.attention.train()
217
+ self.transport.train()
218
+
219
+ frame, _ = batch
220
+ self.start_time = time.time()
221
+
222
+ # Get training losses.
223
+ step = self.total_steps + 1
224
+ loss0, err0 = self.attn_training_step(frame)
225
+
226
+ self.start_time = time.time()
227
+
228
+ if isinstance(self.transport, Attention):
229
+ loss1, err1 = self.attn_training_step(frame)
230
+ else:
231
+ loss1, err1 = self.transport_training_step(frame)
232
+
233
+ total_loss = loss0 + loss1
234
+ self.total_steps = step
235
+ self.start_time = time.time()
236
+ self.log('tr/attn/loss', loss0)
237
+ self.log('tr/trans/loss', loss1)
238
+ self.log('tr/loss', total_loss)
239
+ self.check_save_iteration()
240
+
241
+ return dict(
242
+ loss=total_loss,
243
+ )
244
+
245
+ def check_save_iteration(self):
246
+ global_step = self.total_steps
247
+
248
+ if (global_step + 1) % 100 == 0:
249
+ # save lastest checkpoint
250
+ print(f"Saving last.ckpt Epoch: {self.trainer.current_epoch} | Global Step: {self.trainer.global_step}")
251
+ self.save_last_checkpoint()
252
+
253
+ def save_last_checkpoint(self):
254
+ checkpoint_path = os.path.join(self.cfg['train']['train_dir'], 'checkpoints')
255
+ ckpt_path = os.path.join(checkpoint_path, 'last.ckpt')
256
+ self.trainer.save_checkpoint(ckpt_path)
257
+
258
+ def validation_step(self, batch, batch_idx):
259
+ self.attention.eval()
260
+ self.transport.eval()
261
+
262
+ loss0, loss1 = 0, 0
263
+ assert self.val_repeats >= 1
264
+ for i in range(self.val_repeats):
265
+ frame, _ = batch
266
+ l0, err0 = self.attn_training_step(frame, backprop=False, compute_err=True)
267
+ loss0 += l0
268
+ if isinstance(self.transport, Attention):
269
+ l1, err1 = self.attn_training_step(frame, backprop=False, compute_err=True)
270
+ loss1 += l1
271
+ else:
272
+ l1, err1 = self.transport_training_step(frame, backprop=False, compute_err=True)
273
+ loss1 += l1
274
+ loss0 /= self.val_repeats
275
+ loss1 /= self.val_repeats
276
+ val_total_loss = loss0 + loss1
277
+
278
+ return dict(
279
+ val_loss=val_total_loss,
280
+ val_loss0=loss0,
281
+ val_loss1=loss1,
282
+ val_attn_dist_err=err0['dist'],
283
+ val_attn_theta_err=err0['theta'],
284
+ val_trans_dist_err=err1['dist'],
285
+ val_trans_theta_err=err1['theta'],
286
+ )
287
+
288
+ def training_epoch_end(self, all_outputs):
289
+ super().training_epoch_end(all_outputs)
290
+ utils.set_seed(self.trainer.current_epoch+1)
291
+
292
+ def validation_epoch_end(self, all_outputs):
293
+ mean_val_total_loss = np.mean([v['val_loss'].item() for v in all_outputs])
294
+ mean_val_loss0 = np.mean([v['val_loss0'].item() for v in all_outputs])
295
+ mean_val_loss1 = np.mean([v['val_loss1'].item() for v in all_outputs])
296
+ total_attn_dist_err = np.sum([v['val_attn_dist_err'].sum() for v in all_outputs])
297
+ total_attn_theta_err = np.sum([v['val_attn_theta_err'].sum() for v in all_outputs])
298
+ total_trans_dist_err = np.sum([v['val_trans_dist_err'].sum() for v in all_outputs])
299
+ total_trans_theta_err = np.sum([v['val_trans_theta_err'].sum() for v in all_outputs])
300
+
301
+
302
+ self.log('vl/attn/loss', mean_val_loss0)
303
+ self.log('vl/trans/loss', mean_val_loss1)
304
+ self.log('vl/loss', mean_val_total_loss)
305
+ self.log('vl/total_attn_dist_err', total_attn_dist_err)
306
+ self.log('vl/total_attn_theta_err', total_attn_theta_err)
307
+ self.log('vl/total_trans_dist_err', total_trans_dist_err)
308
+ self.log('vl/total_trans_theta_err', total_trans_theta_err)
309
+
310
+ print("\nAttn Err - Dist: {:.2f}, Theta: {:.2f}".format(total_attn_dist_err, total_attn_theta_err))
311
+ print("Transport Err - Dist: {:.2f}, Theta: {:.2f}".format(total_trans_dist_err, total_trans_theta_err))
312
+
313
+ return dict(
314
+ val_loss=mean_val_total_loss,
315
+ val_loss0=mean_val_loss0,
316
+ mean_val_loss1=mean_val_loss1,
317
+ total_attn_dist_err=total_attn_dist_err,
318
+ total_attn_theta_err=total_attn_theta_err,
319
+ total_trans_dist_err=total_trans_dist_err,
320
+ total_trans_theta_err=total_trans_theta_err,
321
+ )
322
+
323
+ def act(self, obs, info=None, goal=None): # pylint: disable=unused-argument
324
+ """Run inference and return best action given visual observations."""
325
+ # Get heightmap from RGB-D images.
326
+ img = self.test_ds.get_image(obs)
327
+
328
+ # Attention model forward pass.
329
+ pick_inp = {'inp_img': img}
330
+ pick_conf = self.attn_forward(pick_inp)
331
+
332
+
333
+ pick_conf = pick_conf.detach().cpu().numpy()
334
+ argmax = np.argmax(pick_conf)
335
+ argmax = np.unravel_index(argmax, shape=pick_conf.shape)
336
+ p0_pix = argmax[:2]
337
+ p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2])
338
+
339
+ # Transport model forward pass.
340
+ place_inp = {'inp_img': img, 'p0': p0_pix}
341
+ place_conf = self.trans_forward(place_inp)
342
+ place_conf = place_conf.permute(1, 2, 0)
343
+ place_conf = place_conf.detach().cpu().numpy()
344
+ argmax = np.argmax(place_conf)
345
+ argmax = np.unravel_index(argmax, shape=place_conf.shape)
346
+ p1_pix = argmax[:2]
347
+ p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2])
348
+
349
+ # Pixels to end effector poses.
350
+ hmap = img[:, :, 3]
351
+ p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size)
352
+ p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size)
353
+ p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta))
354
+ p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta))
355
+
356
+ return {
357
+ 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)),
358
+ 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)),
359
+ 'pick': p0_pix,
360
+ 'place': p1_pix,
361
+ }
362
+
363
+ def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure, on_tpu, using_native_amp, using_lbfgs):
364
+ pass
365
+
366
+ def configure_optimizers(self):
367
+ pass
368
+
369
+ def train_dataloader(self):
370
+ return self.train_loader
371
+
372
+ def val_dataloader(self):
373
+ return self.test_loader
374
+
375
+ def load(self, model_path):
376
+ self.load_state_dict(torch.load(model_path)['state_dict'])
377
+ self.to(device=self.device_type)
378
+
379
+
380
+ class OriginalTransporterAgent(TransporterAgent):
381
+
382
+ def __init__(self, name, cfg, train_ds, test_ds):
383
+ super().__init__(name, cfg, train_ds, test_ds)
384
+
385
+ def _build_model(self):
386
+ stream_fcn = 'plain_resnet'
387
+ self.attention = Attention(
388
+ stream_fcn=(stream_fcn, None),
389
+ in_shape=self.in_shape,
390
+ n_rotations=1,
391
+ preprocess=utils.preprocess,
392
+ cfg=self.cfg,
393
+ device=self.device_type,
394
+ )
395
+ self.transport = Transport(
396
+ stream_fcn=(stream_fcn, None),
397
+ in_shape=self.in_shape,
398
+ n_rotations=self.n_rotations,
399
+ crop_size=self.crop_size,
400
+ preprocess=utils.preprocess,
401
+ cfg=self.cfg,
402
+ device=self.device_type,
403
+ )
404
+
405
+
406
+ class ClipUNetTransporterAgent(TransporterAgent):
407
+
408
+ def __init__(self, name, cfg, train_ds, test_ds):
409
+ super().__init__(name, cfg, train_ds, test_ds)
410
+
411
+ def _build_model(self):
412
+ stream_fcn = 'clip_unet'
413
+ self.attention = Attention(
414
+ stream_fcn=(stream_fcn, None),
415
+ in_shape=self.in_shape,
416
+ n_rotations=1,
417
+ preprocess=utils.preprocess,
418
+ cfg=self.cfg,
419
+ device=self.device_type,
420
+ )
421
+ self.transport = Transport(
422
+ stream_fcn=(stream_fcn, None),
423
+ in_shape=self.in_shape,
424
+ n_rotations=self.n_rotations,
425
+ crop_size=self.crop_size,
426
+ preprocess=utils.preprocess,
427
+ cfg=self.cfg,
428
+ device=self.device_type,
429
+ )
430
+
431
+
432
+ class TwoStreamClipUNetTransporterAgent(TransporterAgent):
433
+
434
+ def __init__(self, name, cfg, train_ds, test_ds):
435
+ super().__init__(name, cfg, train_ds, test_ds)
436
+
437
+ def _build_model(self):
438
+ stream_one_fcn = 'plain_resnet'
439
+ stream_two_fcn = 'clip_unet'
440
+ self.attention = TwoStreamAttention(
441
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
442
+ in_shape=self.in_shape,
443
+ n_rotations=1,
444
+ preprocess=utils.preprocess,
445
+ cfg=self.cfg,
446
+ device=self.device_type,
447
+ )
448
+ self.transport = TwoStreamTransport(
449
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
450
+ in_shape=self.in_shape,
451
+ n_rotations=self.n_rotations,
452
+ crop_size=self.crop_size,
453
+ preprocess=utils.preprocess,
454
+ cfg=self.cfg,
455
+ device=self.device_type,
456
+ )
457
+
458
+
459
+ class TwoStreamClipUNetLatTransporterAgent(TransporterAgent):
460
+
461
+ def __init__(self, name, cfg, train_ds, test_ds):
462
+ super().__init__(name, cfg, train_ds, test_ds)
463
+
464
+ def _build_model(self):
465
+ stream_one_fcn = 'plain_resnet_lat'
466
+ stream_two_fcn = 'clip_unet_lat'
467
+ self.attention = TwoStreamAttentionLat(
468
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
469
+ in_shape=self.in_shape,
470
+ n_rotations=1,
471
+ preprocess=utils.preprocess,
472
+ cfg=self.cfg,
473
+ device=self.device_type,
474
+ )
475
+ self.transport = TwoStreamTransportLat(
476
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
477
+ in_shape=self.in_shape,
478
+ n_rotations=self.n_rotations,
479
+ crop_size=self.crop_size,
480
+ preprocess=utils.preprocess,
481
+ cfg=self.cfg,
482
+ device=self.device_type,
483
+ )
484
+
485
+
486
+ class TwoStreamClipWithoutSkipsTransporterAgent(TransporterAgent):
487
+
488
+ def __init__(self, name, cfg, train_ds, test_ds):
489
+ super().__init__(name, cfg, train_ds, test_ds)
490
+
491
+ def _build_model(self):
492
+ # TODO: lateral version
493
+ stream_one_fcn = 'plain_resnet'
494
+ stream_two_fcn = 'clip_woskip'
495
+ self.attention = TwoStreamAttention(
496
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
497
+ in_shape=self.in_shape,
498
+ n_rotations=1,
499
+ preprocess=utils.preprocess,
500
+ cfg=self.cfg,
501
+ device=self.device_type,
502
+ )
503
+ self.transport = TwoStreamTransport(
504
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
505
+ in_shape=self.in_shape,
506
+ n_rotations=self.n_rotations,
507
+ crop_size=self.crop_size,
508
+ preprocess=utils.preprocess,
509
+ cfg=self.cfg,
510
+ device=self.device_type,
511
+ )
512
+
513
+
514
+ class TwoStreamRN50BertUNetTransporterAgent(TransporterAgent):
515
+
516
+ def __init__(self, name, cfg, train_ds, test_ds):
517
+ super().__init__(name, cfg, train_ds, test_ds)
518
+
519
+ def _build_model(self):
520
+ # TODO: lateral version
521
+ stream_one_fcn = 'plain_resnet'
522
+ stream_two_fcn = 'rn50_bert_unet'
523
+ self.attention = TwoStreamAttention(
524
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
525
+ in_shape=self.in_shape,
526
+ n_rotations=1,
527
+ preprocess=utils.preprocess,
528
+ cfg=self.cfg,
529
+ device=self.device_type,
530
+ )
531
+ self.transport = TwoStreamTransport(
532
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
533
+ in_shape=self.in_shape,
534
+ n_rotations=self.n_rotations,
535
+ crop_size=self.crop_size,
536
+ preprocess=utils.preprocess,
537
+ cfg=self.cfg,
538
+ device=self.device_type,
539
+ )
cliport/agents/transporter_image_goal.py ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+
3
+ from cliport.utils import utils
4
+ from cliport.agents.transporter import OriginalTransporterAgent
5
+ from cliport.models.core.attention import Attention
6
+ from cliport.models.core.attention_image_goal import AttentionImageGoal
7
+ from cliport.models.core.transport_image_goal import TransportImageGoal
8
+
9
+
10
+ class ImageGoalTransporterAgent(OriginalTransporterAgent):
11
+ def __init__(self, name, cfg, train_ds, test_ds):
12
+ super().__init__(name, cfg, train_ds, test_ds)
13
+
14
+ def _build_model(self):
15
+ stream_fcn = 'plain_resnet'
16
+ self.attention = AttentionImageGoal(
17
+ stream_fcn=(stream_fcn, None),
18
+ in_shape=self.in_shape,
19
+ n_rotations=1,
20
+ preprocess=utils.preprocess,
21
+ cfg=self.cfg,
22
+ device=self.device_type,
23
+ )
24
+ self.transport = TransportImageGoal(
25
+ stream_fcn=(stream_fcn, None),
26
+ in_shape=self.in_shape,
27
+ n_rotations=self.n_rotations,
28
+ crop_size=self.crop_size,
29
+ preprocess=utils.preprocess,
30
+ cfg=self.cfg,
31
+ device=self.device_type,
32
+ )
33
+
34
+ def attn_forward(self, inp, softmax=True):
35
+ inp_img = inp['inp_img']
36
+ goal_img = inp['goal_img']
37
+
38
+ out = self.attention.forward(inp_img, goal_img, softmax=softmax)
39
+ return out
40
+
41
+ def attn_training_step(self, frame, goal, backprop=True, compute_err=False):
42
+ inp_img = frame['img']
43
+ goal_img = goal['img']
44
+ p0, p0_theta = frame['p0'], frame['p0_theta']
45
+
46
+ inp = {'inp_img': inp_img, 'goal_img': goal_img}
47
+ out = self.attn_forward(inp, softmax=False)
48
+ return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta)
49
+
50
+ def trans_forward(self, inp, softmax=True):
51
+ inp_img = inp['inp_img']
52
+ goal_img = inp['goal_img']
53
+ p0 = inp['p0']
54
+
55
+ out = self.transport.forward(inp_img, goal_img, p0, softmax=softmax)
56
+ return out
57
+
58
+ def transport_training_step(self, frame, goal, backprop=True, compute_err=False):
59
+ inp_img = frame['img']
60
+ goal_img = goal['img']
61
+ p0 = frame['p0']
62
+ p1, p1_theta = frame['p1'], frame['p1_theta']
63
+
64
+ inp = {'inp_img': inp_img, 'goal_img': goal_img, 'p0': p0}
65
+ out = self.trans_forward(inp, softmax=False)
66
+ err, loss = self.transport_criterion(backprop, compute_err, inp, out, p0, p1, p1_theta)
67
+ return loss, err
68
+
69
+ def training_step(self, batch, batch_idx):
70
+ self.attention.train()
71
+ self.transport.train()
72
+ frame, goal = batch
73
+
74
+ # Get training losses.
75
+ step = self.total_steps + 1
76
+ loss0, err0 = self.attn_training_step(frame, goal)
77
+ if isinstance(self.transport, Attention):
78
+ loss1, err1 = self.attn_training_step(frame, goal)
79
+ else:
80
+ loss1, err1 = self.transport_training_step(frame, goal)
81
+ total_loss = loss0 + loss1
82
+ self.log('tr/attn/loss', loss0)
83
+ self.log('tr/trans/loss', loss1)
84
+ self.log('tr/loss', total_loss)
85
+ self.total_steps = step
86
+
87
+ self.trainer.train_loop.running_loss.append(total_loss)
88
+
89
+ self.check_save_iteration()
90
+
91
+ return dict(
92
+ loss=total_loss,
93
+ )
94
+
95
+ def validation_step(self, batch, batch_idx):
96
+ self.attention.eval()
97
+ self.transport.eval()
98
+
99
+ loss0, loss1 = 0, 0
100
+ for i in range(self.val_repeats):
101
+ frame, goal = batch
102
+ l0, err0 = self.attn_training_step(frame, goal, backprop=False, compute_err=True)
103
+ loss0 += l0
104
+ if isinstance(self.transport, Attention):
105
+ l1, err1 = self.attn_training_step(frame, goal, backprop=False, compute_err=True)
106
+ loss1 += l1
107
+ else:
108
+ l1, err1 = self.transport_training_step(frame, goal, backprop=False, compute_err=True)
109
+ loss1 += l1
110
+ loss0 /= self.val_repeats
111
+ loss1 /= self.val_repeats
112
+ val_total_loss = loss0 + loss1
113
+
114
+ self.trainer.evaluation_loop.trainer.train_loop.running_loss.append(val_total_loss)
115
+
116
+ return dict(
117
+ val_loss=val_total_loss,
118
+ val_loss0=loss0,
119
+ val_loss1=loss1,
120
+ val_attn_dist_err=err0['dist'],
121
+ val_attn_theta_err=err0['theta'],
122
+ val_trans_dist_err=err1['dist'],
123
+ val_trans_theta_err=err1['theta'],
124
+ )
125
+
126
+ def act(self, obs, info=None, goal=None): # pylint: disable=unused-argument
127
+ """Run inference and return best action given visual observations."""
128
+ # Get heightmap from RGB-D images.
129
+ img = self.test_ds.get_image(obs)
130
+ goal_img = self.test_ds.get_image(goal[0])
131
+
132
+ # Attention model forward pass.
133
+ pick_conf = self.attention.forward(img, goal_img)
134
+ pick_conf = pick_conf.detach().cpu().numpy()
135
+ argmax = np.argmax(pick_conf)
136
+ argmax = np.unravel_index(argmax, shape=pick_conf.shape)
137
+ p0_pix = argmax[:2]
138
+ p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2])
139
+
140
+ # Transport model forward pass.
141
+ place_conf = self.transport.forward(img, goal_img, p0_pix)
142
+ place_conf = place_conf.permute(1, 2, 0)
143
+ place_conf = place_conf.detach().cpu().numpy()
144
+ argmax = np.argmax(place_conf)
145
+ argmax = np.unravel_index(argmax, shape=place_conf.shape)
146
+ p1_pix = argmax[:2]
147
+ p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2])
148
+
149
+ # Pixels to end effector poses.
150
+ hmap = img[:, :, 3]
151
+ p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size)
152
+ p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size)
153
+ p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta))
154
+ p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta))
155
+
156
+ return {
157
+ 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)),
158
+ 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)),
159
+ 'pick': p0_pix,
160
+ 'place': p1_pix,
161
+ }
cliport/agents/transporter_lang_goal.py ADDED
@@ -0,0 +1,386 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import numpy as np
2
+
3
+ from cliport.utils import utils
4
+ from cliport.agents.transporter import TransporterAgent
5
+
6
+ from cliport.models.streams.one_stream_attention_lang_fusion import OneStreamAttentionLangFusion
7
+ from cliport.models.streams.one_stream_transport_lang_fusion import OneStreamTransportLangFusion
8
+ from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusion
9
+ from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusion, TwoStreamTransportLangFusionLatReduce, TwoStreamTransportLangFusionLatPretrained18
10
+ from cliport.models.streams.two_stream_attention_lang_fusion import TwoStreamAttentionLangFusionLat, TwoStreamAttentionLangFusionLatReduce
11
+
12
+ from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusionLatReduceOneStream
13
+ from cliport.models.streams.two_stream_transport_lang_fusion import TwoStreamTransportLangFusionLat
14
+ import torch
15
+ import time
16
+
17
+
18
+ class TwoStreamClipLingUNetTransporterAgent(TransporterAgent):
19
+ def __init__(self, name, cfg, train_ds, test_ds):
20
+ super().__init__(name, cfg, train_ds, test_ds)
21
+
22
+ def _build_model(self):
23
+ stream_one_fcn = 'plain_resnet'
24
+ stream_two_fcn = 'clip_lingunet'
25
+ self.attention = TwoStreamAttentionLangFusion(
26
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
27
+ in_shape=self.in_shape,
28
+ n_rotations=1,
29
+ preprocess=utils.preprocess,
30
+ cfg=self.cfg,
31
+ device=self.device_type,
32
+ )
33
+ self.transport = TwoStreamTransportLangFusion(
34
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
35
+ in_shape=self.in_shape,
36
+ n_rotations=self.n_rotations,
37
+ crop_size=self.crop_size,
38
+ preprocess=utils.preprocess,
39
+ cfg=self.cfg,
40
+ device=self.device_type,
41
+ )
42
+
43
+ def attn_forward(self, inp, softmax=True):
44
+ inp_img = inp['inp_img']
45
+ if type(inp_img) is not torch.Tensor:
46
+ inp_img = torch.from_numpy(inp_img).to('cuda').float().contiguous()
47
+ lang_goal = inp['lang_goal']
48
+
49
+ out = self.attention.forward(inp_img.float(), lang_goal, softmax=softmax)
50
+ return out
51
+
52
+ def attn_training_step(self, frame, backprop=True, compute_err=False):
53
+ inp_img = frame['img']
54
+ if type(inp_img) is not torch.Tensor:
55
+ inp_img = torch.from_numpy(inp_img).to('cuda').float()
56
+ p0, p0_theta = frame['p0'], frame['p0_theta']
57
+ lang_goal = frame['lang_goal']
58
+
59
+ inp = {'inp_img': inp_img, 'lang_goal': lang_goal}
60
+ out = self.attn_forward(inp, softmax=False)
61
+ return self.attn_criterion(backprop, compute_err, inp, out, p0, p0_theta)
62
+
63
+ def trans_forward(self, inp, softmax=True):
64
+ inp_img = inp['inp_img']
65
+ if type(inp_img) is not torch.Tensor:
66
+ inp_img = torch.from_numpy(inp_img).to('cuda').float()
67
+ p0 = inp['p0']
68
+ lang_goal = inp['lang_goal']
69
+ out = self.transport.forward(inp_img.float(), p0, lang_goal, softmax=softmax)
70
+ return out
71
+
72
+ def transport_training_step(self, frame, backprop=True, compute_err=False):
73
+ inp_img = frame['img']
74
+ p0 = frame['p0']
75
+ p1, p1_theta = frame['p1'], frame['p1_theta']
76
+ lang_goal = frame['lang_goal']
77
+
78
+ inp = {'inp_img': inp_img, 'p0': p0, 'lang_goal': lang_goal}
79
+ out = self.trans_forward(inp, softmax=False)
80
+ err, loss = self.transport_criterion(backprop, compute_err, inp, out, p0, p1, p1_theta)
81
+ return loss, err
82
+
83
+ def act(self, obs, info, goal=None): # pylint: disable=unused-argument
84
+ """Run inference and return best action given visual observations."""
85
+ # Get heightmap from RGB-D images.
86
+ img = self.test_ds.get_image(obs)
87
+ lang_goal = info['lang_goal']
88
+
89
+ # Attention model forward pass.
90
+ pick_inp = {'inp_img': img, 'lang_goal': lang_goal}
91
+ pick_conf = self.attn_forward(pick_inp)
92
+ pick_conf = pick_conf[0].permute(1, 2, 0).detach().cpu().numpy()
93
+ #
94
+ argmax = np.argmax(pick_conf)
95
+ # import IPython; IPython.embed()
96
+ argmax = np.unravel_index(argmax, shape=pick_conf.shape)
97
+ p0_pix = argmax[:2]
98
+ p0_theta = argmax[2] * (2 * np.pi / pick_conf.shape[2])
99
+
100
+ # Transport model forward pass.
101
+ place_inp = {'inp_img': img, 'p0': p0_pix, 'lang_goal': lang_goal}
102
+ place_conf = self.trans_forward(place_inp)
103
+ place_conf = place_conf.squeeze().permute(1, 2, 0)
104
+ place_conf = place_conf.detach().cpu().numpy()
105
+ argmax = np.argmax(place_conf)
106
+ argmax = np.unravel_index(argmax, shape=place_conf.shape)
107
+ p1_pix = argmax[:2]
108
+ p1_theta = argmax[2] * (2 * np.pi / place_conf.shape[2])
109
+
110
+ # Pixels to end effector poses.
111
+ hmap = img[:, :, 3]
112
+ p0_xyz = utils.pix_to_xyz(p0_pix, hmap, self.bounds, self.pix_size)
113
+ p1_xyz = utils.pix_to_xyz(p1_pix, hmap, self.bounds, self.pix_size)
114
+ p0_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p0_theta))
115
+ p1_xyzw = utils.eulerXYZ_to_quatXYZW((0, 0, -p1_theta))
116
+
117
+ return {
118
+ 'pose0': (np.asarray(p0_xyz), np.asarray(p0_xyzw)),
119
+ 'pose1': (np.asarray(p1_xyz), np.asarray(p1_xyzw)),
120
+ 'pick': [p0_pix[0], p0_pix[1], p0_theta],
121
+ 'place': [p1_pix[0], p1_pix[1], p1_theta],
122
+ }
123
+
124
+
125
+ class TwoStreamClipFilmLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent):
126
+ def __init__(self, name, cfg, train_ds, test_ds):
127
+ super().__init__(name, cfg, train_ds, test_ds)
128
+
129
+ def _build_model(self):
130
+ stream_one_fcn = 'plain_resnet_lat'
131
+ stream_two_fcn = 'clip_film_lingunet_lat'
132
+ self.attention = TwoStreamAttentionLangFusionLat(
133
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
134
+ in_shape=self.in_shape,
135
+ n_rotations=1,
136
+ preprocess=utils.preprocess,
137
+ cfg=self.cfg,
138
+ device=self.device_type,
139
+ )
140
+ self.transport = TwoStreamTransportLangFusionLat(
141
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
142
+ in_shape=self.in_shape,
143
+ n_rotations=self.n_rotations,
144
+ crop_size=self.crop_size,
145
+ preprocess=utils.preprocess,
146
+ cfg=self.cfg,
147
+ device=self.device_type,
148
+ )
149
+
150
+
151
+ class TwoStreamClipLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent): # This is our model
152
+ def __init__(self, name, cfg, train_ds, test_ds):
153
+ super().__init__(name, cfg, train_ds, test_ds)
154
+
155
+ def _build_model(self):
156
+ stream_one_fcn = 'plain_resnet_lat'
157
+ stream_two_fcn = 'clip_lingunet_lat'
158
+ self.attention = TwoStreamAttentionLangFusionLat(
159
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
160
+ in_shape=self.in_shape,
161
+ n_rotations=1,
162
+ preprocess=utils.preprocess,
163
+ cfg=self.cfg,
164
+ device=self.device_type,
165
+ )
166
+ self.transport = TwoStreamTransportLangFusionLat(
167
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
168
+ in_shape=self.in_shape,
169
+ n_rotations=self.n_rotations,
170
+ crop_size=self.crop_size,
171
+ preprocess=utils.preprocess,
172
+ cfg=self.cfg,
173
+ device=self.device_type,
174
+ )
175
+
176
+
177
+
178
+ class TwoStreamClipLingUNetLatTransporterAgentReduce(TwoStreamClipLingUNetTransporterAgent): # This is our model
179
+ def __init__(self, name, cfg, train_ds, test_ds):
180
+ super().__init__(name, cfg, train_ds, test_ds)
181
+
182
+ def _build_model(self):
183
+ stream_one_fcn = 'plain_resnet_lat'
184
+ stream_two_fcn = 'clip_lingunet_lat'
185
+ self.attention = TwoStreamAttentionLangFusionLat(
186
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
187
+ in_shape=self.in_shape,
188
+ n_rotations=1,
189
+ preprocess=utils.preprocess,
190
+ cfg=self.cfg,
191
+ device=self.device_type,
192
+ )
193
+ self.transport = TwoStreamTransportLangFusionLatReduce(
194
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
195
+ in_shape=self.in_shape,
196
+ n_rotations=self.n_rotations,
197
+ crop_size=self.crop_size,
198
+ preprocess=utils.preprocess,
199
+ cfg=self.cfg,
200
+ device=self.device_type,
201
+ )
202
+
203
+
204
+
205
+ class TwoStreamClipLingUNetLatTransporterAgentReduceOneStream(TwoStreamClipLingUNetTransporterAgent): # This is our model
206
+ def __init__(self, name, cfg, train_ds, test_ds):
207
+ super().__init__(name, cfg, train_ds, test_ds)
208
+
209
+ def _build_model(self):
210
+ stream_one_fcn = 'plain_resnet_lat'
211
+ stream_two_fcn = 'clip_lingunet_lat'
212
+ self.attention = TwoStreamAttentionLangFusionLatReduce(
213
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
214
+ in_shape=self.in_shape,
215
+ n_rotations=1,
216
+ preprocess=utils.preprocess,
217
+ cfg=self.cfg,
218
+ device=self.device_type,
219
+ )
220
+ self.transport = TwoStreamTransportLangFusionLatReduceOneStream(
221
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
222
+ in_shape=self.in_shape,
223
+ n_rotations=self.n_rotations,
224
+ crop_size=self.crop_size,
225
+ preprocess=utils.preprocess,
226
+ cfg=self.cfg,
227
+ device=self.device_type,
228
+ )
229
+
230
+
231
+ class TwoStreamClipLingUNetLatTransporterAgentReducePretrained(TwoStreamClipLingUNetTransporterAgent): # This is our model
232
+ def __init__(self, name, cfg, train_ds, test_ds):
233
+ super().__init__(name, cfg, train_ds, test_ds)
234
+
235
+ def _build_model(self):
236
+ stream_one_fcn = 'plain_resnet_lat'
237
+ stream_two_fcn = 'clip_lingunet_lat'
238
+ self.attention = TwoStreamAttentionLangFusionLat(
239
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
240
+ in_shape=self.in_shape,
241
+ n_rotations=1,
242
+ preprocess=utils.preprocess,
243
+ cfg=self.cfg,
244
+ device=self.device_type,
245
+ )
246
+ self.transport = TwoStreamTransportLangFusionLatPretrained18(
247
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
248
+ in_shape=self.in_shape,
249
+ n_rotations=self.n_rotations,
250
+ crop_size=self.crop_size,
251
+ preprocess=utils.preprocess,
252
+ cfg=self.cfg,
253
+ device=self.device_type,
254
+ )
255
+
256
+
257
+
258
+ class TwoStreamRN50BertLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent):
259
+ def __init__(self, name, cfg, train_ds, test_ds):
260
+ super().__init__(name, cfg, train_ds, test_ds)
261
+
262
+ def _build_model(self):
263
+ stream_one_fcn = 'plain_resnet'
264
+ stream_two_fcn = 'rn50_bert_lingunet'
265
+ self.attention = TwoStreamAttentionLangFusion(
266
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
267
+ in_shape=self.in_shape,
268
+ n_rotations=1,
269
+ preprocess=utils.preprocess,
270
+ cfg=self.cfg,
271
+ device=self.device_type,
272
+ )
273
+ self.transport = TwoStreamTransportLangFusion(
274
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
275
+ in_shape=self.in_shape,
276
+ n_rotations=self.n_rotations,
277
+ crop_size=self.crop_size,
278
+ preprocess=utils.preprocess,
279
+ cfg=self.cfg,
280
+ device=self.device_type,
281
+ )
282
+
283
+
284
+ class TwoStreamUntrainedRN50BertLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent):
285
+ def __init__(self, name, cfg, train_ds, test_ds):
286
+ super().__init__(name, cfg, train_ds, test_ds)
287
+
288
+ def _build_model(self):
289
+ stream_one_fcn = 'plain_resnet'
290
+ stream_two_fcn = 'untrained_rn50_bert_lingunet'
291
+ self.attention = TwoStreamAttentionLangFusion(
292
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
293
+ in_shape=self.in_shape,
294
+ n_rotations=1,
295
+ preprocess=utils.preprocess,
296
+ cfg=self.cfg,
297
+ device=self.device_type,
298
+ )
299
+ self.transport = TwoStreamTransportLangFusion(
300
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
301
+ in_shape=self.in_shape,
302
+ n_rotations=self.n_rotations,
303
+ crop_size=self.crop_size,
304
+ preprocess=utils.preprocess,
305
+ cfg=self.cfg,
306
+ device=self.device_type,
307
+ )
308
+
309
+
310
+ class TwoStreamRN50BertLingUNetLatTransporterAgent(TwoStreamClipLingUNetTransporterAgent):
311
+ def __init__(self, name, cfg, train_ds, test_ds):
312
+ super().__init__(name, cfg, train_ds, test_ds)
313
+
314
+ def _build_model(self):
315
+ stream_one_fcn = 'plain_resnet_lat'
316
+ stream_two_fcn = 'rn50_bert_lingunet_lat'
317
+ self.attention = TwoStreamAttentionLangFusionLat(
318
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
319
+ in_shape=self.in_shape,
320
+ n_rotations=1,
321
+ preprocess=utils.preprocess,
322
+ cfg=self.cfg,
323
+ device=self.device_type,
324
+ )
325
+ self.transport = TwoStreamTransportLangFusionLat(
326
+ stream_fcn=(stream_one_fcn, stream_two_fcn),
327
+ in_shape=self.in_shape,
328
+ n_rotations=self.n_rotations,
329
+ crop_size=self.crop_size,
330
+ preprocess=utils.preprocess,
331
+ cfg=self.cfg,
332
+ device=self.device_type,
333
+ )
334
+
335
+
336
+ class OriginalTransporterLangFusionAgent(TwoStreamClipLingUNetTransporterAgent):
337
+
338
+ def __init__(self, name, cfg, train_ds, test_ds):
339
+ super().__init__(name, cfg, train_ds, test_ds)
340
+
341
+ def _build_model(self):
342
+ stream_fcn = 'plain_resnet_lang'
343
+ self.attention = OneStreamAttentionLangFusion(
344
+ stream_fcn=(stream_fcn, None),
345
+ in_shape=self.in_shape,
346
+ n_rotations=1,
347
+ preprocess=utils.preprocess,
348
+ cfg=self.cfg,
349
+ device=self.device_type,
350
+ )
351
+ self.transport = OneStreamTransportLangFusion(
352
+ stream_fcn=(stream_fcn, None),
353
+ in_shape=self.in_shape,
354
+ n_rotations=self.n_rotations,
355
+ crop_size=self.crop_size,
356
+ preprocess=utils.preprocess,
357
+ cfg=self.cfg,
358
+ device=self.device_type,
359
+ )
360
+
361
+
362
+
363
+ class ClipLingUNetTransporterAgent(TwoStreamClipLingUNetTransporterAgent):
364
+
365
+ def __init__(self, name, cfg, train_ds, test_ds):
366
+ super().__init__(name, cfg, train_ds, test_ds)
367
+
368
+ def _build_model(self):
369
+ stream_fcn = 'clip_lingunet'
370
+ self.attention = OneStreamAttentionLangFusion(
371
+ stream_fcn=(stream_fcn, None),
372
+ in_shape=self.in_shape,
373
+ n_rotations=1,
374
+ preprocess=utils.preprocess,
375
+ cfg=self.cfg,
376
+ device=self.device_type,
377
+ )
378
+ self.transport = OneStreamTransportLangFusion(
379
+ stream_fcn=(stream_fcn, None),
380
+ in_shape=self.in_shape,
381
+ n_rotations=self.n_rotations,
382
+ crop_size=self.crop_size,
383
+ preprocess=utils.preprocess,
384
+ cfg=self.cfg,
385
+ device=self.device_type,
386
+ )
cliport/cfg/config.yaml ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # @package _global_
2
+ root_dir: ${oc.env:GENSIM_ROOT} # set this ENV variable if you didn't `python setup.py develop`
3
+
4
+ tag: default
5
+ debug: False
6
+ gpt_temperature: 0.8 # GPT-4 response temperature. higher means more diversity
7
+ prompt_folder: vanilla_task_generation_prompt # the prompt folder that stores the prompt chain
8
+ max_env_run_cnt: 3 # maximum number of runs for each environment
9
+ trials: 10 # how many times of spawning each environment generated
10
+ output_folder: 'output/output_stats'
11
+ model_output_dir: '' # to be filled in with date
12
+ gpt_model: "gpt-4-0613" # which openai gpt model to use
13
+ openai_key: ${oc.env:OPENAI_KEY}
14
+
15
+ # Advanced options
16
+ task_description_candidate_num: -1 # the number of sample task descriptions. -1 means all
17
+ task_asset_candidate_num: -1 # the number of sample task descriptions. -1 means all
18
+ task_code_candidate_num: 4 # the number of sample task code. -1 means all
19
+
20
+
21
+ # Save and Load Memory
22
+ prompt_data_path: prompts/data/
23
+ save_memory: False # save the assets, task code, task descriptions generated offline
24
+ load_memory: False # load the assets, task code, task descriptions generated offline
25
+ use_template: False # use template when constructing prompts, better for scaling
26
+ reflection_agreement_num: 2 # how many models that need to agree to add a new task in reflection
27
+
28
+ target_task_name: "" # specific desired task name
29
+ save_code_early: False # ignore test and save the code after implementation
30
+ load_task_num: -1 # how many tasks to load from offline
cliport/cfg/data.yaml ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Data Generation
2
+
3
+ defaults:
4
+ - config
5
+
6
+ hydra:
7
+ run:
8
+ dir: ${root_dir}
9
+
10
+ data_dir: ${root_dir}/data # where to store dataset
11
+ assets_root: ${root_dir}/cliport/environments/assets/
12
+ disp: False # visualize PyBullet
13
+ shared_memory: False
14
+ task: packing-boxes-pairs-seen-colors
15
+ mode: train # 'train' or 'val' or 'test'
16
+ n: 1000 # number of demos to generate
17
+ save_data: True # write episodes to disk
18
+
19
+ dataset:
20
+ type: 'single' # 'single' or 'multi'
21
+ images: True
22
+ cache: True # load episodes to memory instead of reading from disk
23
+ augment:
24
+ theta_sigma: 60 # rotation sigma in degrees; N(mu = 0, sigma = theta_sigma).
25
+
26
+ # record videos (super slow)
27
+ record:
28
+ save_video: False
29
+ save_video_path: ${data_dir}/${task}-${mode}/videos/
30
+ add_text: False
31
+ add_task_text: True
32
+ fps: 20
33
+ video_height: 640
34
+ video_width: 720
cliport/cfg/eval.yaml ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Evaluation
2
+
3
+ defaults:
4
+ - config
5
+
6
+ hydra:
7
+ run:
8
+ dir: ${root_dir}
9
+
10
+ mode: val # 'val' or 'test'
11
+
12
+ # eval settings
13
+ agent: cliport
14
+ n_demos: 100 # number of val instances
15
+ train_demos: 100 # training demos used to train model
16
+ n_repeats: 1 # number of repeats
17
+ gpu: [0]
18
+ save_results: True # write results to json
19
+ update_results: False # overwrite existing json results?
20
+ checkpoint_type: 'val_missing'
21
+ val_on_heldout: True
22
+
23
+ disp: False
24
+ shared_memory: False
25
+ eval_task: packing-boxes-pairs-seen-colors # task to evaluate the model on
26
+ model_task: ${eval_task} # task the model was trained on (e.g. multi-language-conditioned or packing-boxes-pairs-seen-colors)
27
+ type: single # 'single' or 'multi'
28
+
29
+ # paths
30
+ model_dir: ${root_dir}
31
+ exp_folder: exps
32
+ data_dir: ${root_dir}/data
33
+ assets_root: ${root_dir}/cliport/environments/assets/
34
+
35
+ model_path: ${model_dir}/${exp_folder}/${model_task}-${agent}-n${train_demos}-train/checkpoints/ # path to pre-trained models
36
+ train_config: ${model_dir}/${exp_folder}/${model_task}-${agent}-n${train_demos}-train/.hydra/config.yaml # path to train config
37
+ save_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/checkpoints/ # path to save results
38
+ results_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/checkpoints/ # path to existing results
39
+
40
+
41
+ # record videos (super slow)
42
+ record:
43
+ save_video: False
44
+ save_video_path: ${model_dir}/${exp_folder}/${eval_task}-${agent}-n${train_demos}-train/videos/
45
+ add_text: True
46
+ fps: 20
47
+ video_height: 640
48
+ video_width: 720
49
+ add_task_text: False
50
+ blender_render: False # new: use blender recorder for rendering
cliport/cfg/train.yaml ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Training
2
+
3
+ defaults:
4
+ - config
5
+
6
+ hydra:
7
+ run:
8
+ dir: ${train.train_dir}
9
+
10
+ dataset:
11
+ type: 'single' # 'single' or 'multi'
12
+ images: True
13
+ cache: True # load episodes to memory instead of reading from disk
14
+ augment:
15
+ theta_sigma: 60 # rotation sigma in degrees; N(mu = 0, sigma = theta_sigma).
16
+
17
+ train:
18
+ # folders
19
+ model_task: ${train.task}
20
+ exp_folder: exps
21
+ train_dir: ${root_dir}/${train.exp_folder}/${train.model_task}-${train.agent}-n${train.n_demos}-train
22
+ data_dir: ${root_dir}/data
23
+
24
+ # task configs
25
+ task: packing-boxes-pairs-seen-colors
26
+ agent: two_stream_full_clip_lingunet_lat_transporter
27
+ n_demos: 100
28
+ n_steps: 61000 # original paper use 200000 for single task and use 601000 for multi-task models
29
+
30
+ # hyper params
31
+ n_rotations: 36
32
+ batch_size: 8
33
+ batchnorm: False # important: False because batch_size=1
34
+ lr: 1e-4
35
+
36
+ attn_stream_fusion_type: 'add'
37
+ trans_stream_fusion_type: 'conv'
38
+ lang_fusion_type: 'mult'
39
+ training_step_scale: 200 # How many epochs are needed. 100 data sample requires 20000 steps. -1 means ignored.
40
+
41
+ # script configs
42
+ gpu: -1 # -1 for all
43
+ log: False # log metrics and stats to wandb
44
+ n_val: 10
45
+ val_repeats: 1
46
+ save_steps: [1000, 2000, 3000, 4000, 5000, 7000, 10000, 20000, 40000, 80000, 120000, 160000, 200000, 300000, 400000, 500000, 600000, 800000, 1000000, 1200000]
47
+ load_from_last_ckpt: False # still change to True
48
+
49
+ wandb:
50
+ run_name: 'cliport0'
51
+ logger:
52
+ entity: cliport
53
+ project: cliport
54
+ tags: []
55
+ group: train
56
+ offline: False
57
+ saver:
58
+ upload: False
59
+ monitor: 'val_loss'
cliport/dataset.py ADDED
@@ -0,0 +1,940 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Image dataset."""
2
+
3
+ import os
4
+ import pickle
5
+ import warnings
6
+
7
+ import numpy as np
8
+ from torch.utils.data import Dataset
9
+
10
+ from cliport import tasks
11
+ from cliport.tasks import cameras
12
+ from cliport.utils import utils
13
+ import traceback
14
+
15
+ # See transporter.py, regression.py, dummy.py, task.py, etc.
16
+ PIXEL_SIZE = 0.003125
17
+ CAMERA_CONFIG = cameras.RealSenseD415.CONFIG
18
+ BOUNDS = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
19
+
20
+ # Names as strings, REVERSE-sorted so longer (more specific) names are first.
21
+ TASK_NAMES = (tasks.names).keys()
22
+ TASK_NAMES = sorted(TASK_NAMES)[::-1]
23
+
24
+
25
+ class RavensDataset(Dataset):
26
+ """A simple image dataset class."""
27
+
28
+ def __init__(self, path, cfg, n_demos=0, augment=False):
29
+ """A simple RGB-D image dataset."""
30
+ self._path = path
31
+
32
+ self.cfg = cfg
33
+ self.sample_set = []
34
+ self.max_seed = -1
35
+ self.n_episodes = 0
36
+ self.images = self.cfg['dataset']['images']
37
+ self.cache = self.cfg['dataset']['cache']
38
+ self.n_demos = n_demos
39
+ self.augment = augment
40
+
41
+ self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added
42
+ self.pix_size = 0.003125
43
+ self.in_shape = (320, 160, 6)
44
+ self.cam_config = cameras.RealSenseD415.CONFIG
45
+ self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
46
+
47
+ # Track existing dataset if it exists.
48
+ color_path = os.path.join(self._path, 'action')
49
+ if os.path.exists(color_path):
50
+ for fname in sorted(os.listdir(color_path)):
51
+ if '.pkl' in fname:
52
+ seed = int(fname[(fname.find('-') + 1):-4])
53
+ self.n_episodes += 1
54
+ self.max_seed = max(self.max_seed, seed)
55
+
56
+ self._cache = {}
57
+
58
+ if self.n_demos > 0:
59
+ self.images = self.cfg['dataset']['images']
60
+ self.cache = self.cfg['dataset']['cache']
61
+
62
+ # Check if there sufficient demos in the dataset
63
+ if self.n_demos > self.n_episodes:
64
+ # raise Exception(f"Requested training on {self.n_demos} demos, but only {self.n_episodes} demos exist in the dataset path: {self._path}.")
65
+ print(f"Requested training on {self.n_demos} demos, but only {self.n_episodes} demos exist in the dataset path: {self._path}.")
66
+ self.n_demos = self.n_episodes
67
+
68
+ episodes = np.random.choice(range(self.n_episodes), self.n_demos, False)
69
+ self.set(episodes)
70
+
71
+
72
+ def add(self, seed, episode):
73
+ """Add an episode to the dataset.
74
+
75
+ Args:
76
+ seed: random seed used to initialize the episode.
77
+ episode: list of (obs, act, reward, info) tuples.
78
+ """
79
+ color, depth, action, reward, info = [], [], [], [], []
80
+ for obs, act, r, i in episode:
81
+ color.append(obs['color'])
82
+ depth.append(obs['depth'])
83
+ action.append(act)
84
+ reward.append(r)
85
+ info.append(i)
86
+
87
+ color = np.uint8(color)
88
+ depth = np.float32(depth)
89
+
90
+ def dump(data, field):
91
+ field_path = os.path.join(self._path, field)
92
+ if not os.path.exists(field_path):
93
+ os.makedirs(field_path)
94
+ fname = f'{self.n_episodes:06d}-{seed}.pkl' # -{len(episode):06d}
95
+ with open(os.path.join(field_path, fname), 'wb') as f:
96
+ pickle.dump(data, f)
97
+
98
+ dump(color, 'color')
99
+ dump(depth, 'depth')
100
+ dump(action, 'action')
101
+ dump(reward, 'reward')
102
+ dump(info, 'info')
103
+
104
+ self.n_episodes += 1
105
+ self.max_seed = max(self.max_seed, seed)
106
+
107
+ def set(self, episodes):
108
+ """Limit random samples to specific fixed set."""
109
+ self.sample_set = episodes
110
+
111
+ def load(self, episode_id, images=True, cache=False):
112
+ # TODO(lirui): consider loading into memory
113
+ def load_field(episode_id, field, fname):
114
+
115
+ # Check if sample is in cache.
116
+ if cache:
117
+ if episode_id in self._cache:
118
+ if field in self._cache[episode_id]:
119
+ return self._cache[episode_id][field]
120
+ else:
121
+ self._cache[episode_id] = {}
122
+
123
+ # Load sample from files.
124
+ path = os.path.join(self._path, field)
125
+ data = pickle.load(open(os.path.join(path, fname), 'rb'))
126
+ if cache:
127
+ self._cache[episode_id][field] = data
128
+ return data
129
+
130
+ # Get filename and random seed used to initialize episode.
131
+ seed = None
132
+ path = os.path.join(self._path, 'action')
133
+ for fname in sorted(os.listdir(path)):
134
+ if f'{episode_id:06d}' in fname:
135
+ seed = int(fname[(fname.find('-') + 1):-4])
136
+
137
+ # Load data.
138
+ color = load_field(episode_id, 'color', fname)
139
+ depth = load_field(episode_id, 'depth', fname)
140
+ action = load_field(episode_id, 'action', fname)
141
+ reward = load_field(episode_id, 'reward', fname)
142
+ info = load_field(episode_id, 'info', fname)
143
+
144
+ # Reconstruct episode.
145
+ episode = []
146
+ for i in range(len(action)):
147
+ obs = {'color': color[i], 'depth': depth[i]} if images else {}
148
+ episode.append((obs, action[i], reward[i], info[i]))
149
+ return episode, seed
150
+
151
+ print(f'{episode_id:06d} not in ', path)
152
+
153
+ def get_image(self, obs, cam_config=None):
154
+ """Stack color and height images image."""
155
+
156
+ # if self.use_goal_image:
157
+ # colormap_g, heightmap_g = utils.get_fused_heightmap(goal, configs)
158
+ # goal_image = self.concatenate_c_h(colormap_g, heightmap_g)
159
+ # input_image = np.concatenate((input_image, goal_image), axis=2)
160
+ # assert input_image.shape[2] == 12, input_image.shape
161
+
162
+ if cam_config is None:
163
+ cam_config = self.cam_config
164
+
165
+ # Get color and height maps from RGB-D images.
166
+ cmap, hmap = utils.get_fused_heightmap(
167
+ obs, cam_config, self.bounds, self.pix_size)
168
+ img = np.concatenate((cmap,
169
+ hmap[Ellipsis, None],
170
+ hmap[Ellipsis, None],
171
+ hmap[Ellipsis, None]), axis=2)
172
+ assert img.shape == self.in_shape, img.shape
173
+ return img
174
+
175
+ def process_sample(self, datum, augment=True):
176
+ # Get training labels from data sample.
177
+ (obs, act, _, info) = datum
178
+ img = self.get_image(obs)
179
+
180
+ # p0, p1 = None, None
181
+ # p0_theta, p1_theta = None, None
182
+ # perturb_params = None
183
+ p0, p1 = np.zeros(1), np.zeros(1)
184
+ p0_theta, p1_theta = np.zeros(1), np.zeros(1)
185
+ perturb_params = np.zeros(5)
186
+
187
+ if act:
188
+ p0_xyz, p0_xyzw = act['pose0']
189
+ p1_xyz, p1_xyzw = act['pose1']
190
+ p0 = utils.xyz_to_pix(p0_xyz, self.bounds, self.pix_size)
191
+ p0_theta = -np.float32(utils.quatXYZW_to_eulerXYZ(p0_xyzw)[2])
192
+ p1 = utils.xyz_to_pix(p1_xyz, self.bounds, self.pix_size)
193
+ p1_theta = -np.float32(utils.quatXYZW_to_eulerXYZ(p1_xyzw)[2])
194
+ p1_theta = p1_theta - p0_theta
195
+ p0_theta = 0
196
+
197
+ # Data augmentation.
198
+ if augment:
199
+ img, _, (p0, p1), perturb_params = utils.perturb(img, [p0, p1], theta_sigma=self.aug_theta_sigma)
200
+
201
+ # print("sample", p0,p1,p0_theta,p1_theta,perturb_params)
202
+ sample = {
203
+ 'img': img.copy(),
204
+ 'p0': np.array(p0).copy(), 'p0_theta': np.array(p0_theta).copy(),
205
+ 'p1': np.array(p1).copy(), 'p1_theta': np.array(p1_theta).copy() ,
206
+ 'perturb_params': np.array(perturb_params).copy()
207
+ }
208
+
209
+ # Add language goal if available.
210
+ if 'lang_goal' not in info:
211
+ warnings.warn("No language goal. Defaulting to 'task completed.'")
212
+
213
+ if info and 'lang_goal' in info:
214
+ sample['lang_goal'] = info['lang_goal']
215
+ else:
216
+ sample['lang_goal'] = "task completed."
217
+
218
+ return sample
219
+
220
+ def process_goal(self, goal, perturb_params):
221
+ # Get goal sample.
222
+ (obs, act, _, info) = goal
223
+ img = self.get_image(obs)
224
+
225
+ # p0, p1 = None, None
226
+ # p0_theta, p1_theta = None, None
227
+
228
+ p0, p1 = np.zeros(1), np.zeros(1)
229
+ p0_theta, p1_theta = np.zeros(1), np.zeros(1)
230
+
231
+ # Data augmentation with specific params.
232
+ # try:
233
+ if perturb_params is not None and len(perturb_params) > 1:
234
+ img = utils.apply_perturbation(img, perturb_params)
235
+
236
+ sample = {
237
+ 'img': img.copy(),
238
+ 'p0': p0 , 'p0_theta': np.array(p0_theta).copy(),
239
+ 'p1': p1, 'p1_theta': np.array(p1_theta).copy(),
240
+ 'perturb_params': np.array(perturb_params).copy()
241
+ }
242
+
243
+ # Add language goal if available.
244
+ if 'lang_goal' not in info:
245
+ warnings.warn("No language goal. Defaulting to 'task completed.'")
246
+ # print("goal",p0,p1,p0_theta,p1_theta,perturb_params)
247
+
248
+ if info and 'lang_goal' in info:
249
+ sample['lang_goal'] = info['lang_goal']
250
+ else:
251
+ sample['lang_goal'] = "task completed."
252
+
253
+ return sample
254
+
255
+ def __len__(self):
256
+ return len(self.sample_set)
257
+
258
+ def __getitem__(self, idx):
259
+ # Choose random episode.
260
+ # if len(self.sample_set) > 0:
261
+ # episode_id = np.random.choice(self.sample_set)
262
+ # else:
263
+ # episode_id = np.random.choice(range(self.n_episodes))
264
+ episode_id = self.sample_set[idx]
265
+ res = self.load(episode_id, self.images, self.cache)
266
+ if res is None:
267
+ print("in get item", episode_id, self._path)
268
+ print("load sample return None. Reload")
269
+ print("Exception:", str(traceback.format_exc()))
270
+ return self[0] #
271
+
272
+ episode, _ = res
273
+ # Is the task sequential like stack-block-pyramid-seq?
274
+ is_sequential_task = '-seq' in self._path.split("/")[-1]
275
+
276
+ # Return random observation action pair (and goal) from episode.
277
+ i = np.random.choice(range(len(episode)-1))
278
+ g = i+1 if is_sequential_task else -1
279
+ sample, goal = episode[i], episode[g]
280
+
281
+ # Process sample.
282
+ sample = self.process_sample(sample, augment=self.augment)
283
+ goal = self.process_goal(goal, perturb_params=sample['perturb_params'])
284
+ return sample, goal
285
+
286
+
287
+ class RavensMultiTaskDataset(RavensDataset):
288
+
289
+
290
+ def __init__(self, path, cfg, group='multi-all',
291
+ mode='train', n_demos=100, augment=False):
292
+ """A multi-task dataset."""
293
+ self.root_path = path
294
+ self.mode = mode
295
+ if group not in self.MULTI_TASKS:
296
+ # generate the groups on the fly
297
+ self.tasks = list(set(group)) # .split(" ")
298
+ else:
299
+ self.tasks = self.MULTI_TASKS[group][mode]
300
+
301
+ print("self.tasks:", self.tasks)
302
+ self.attr_train_task = self.MULTI_TASKS[group]['attr_train_task'] if group in self.MULTI_TASKS and 'attr_train_task' in self.MULTI_TASKS[group] else None
303
+
304
+ self.cfg = cfg
305
+ self.sample_set = {}
306
+ self.max_seed = -1
307
+ self.n_episodes = 0
308
+ self.images = self.cfg['dataset']['images']
309
+ self.cache = self.cfg['dataset']['cache']
310
+ self.n_demos = n_demos
311
+ self.augment = augment
312
+
313
+ self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added
314
+ self.pix_size = 0.003125
315
+ self.in_shape = (320, 160, 6)
316
+ self.cam_config = cameras.RealSenseD415.CONFIG
317
+ self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
318
+
319
+ self.n_episodes = {}
320
+ episodes = {}
321
+
322
+ for task in self.tasks:
323
+ task_path = os.path.join(self.root_path, f'{task}-{mode}')
324
+ action_path = os.path.join(task_path, 'action')
325
+ n_episodes = 0
326
+ if os.path.exists(action_path):
327
+ for fname in sorted(os.listdir(action_path)):
328
+ if '.pkl' in fname:
329
+ n_episodes += 1
330
+ self.n_episodes[task] = n_episodes
331
+
332
+ if n_episodes == 0:
333
+ raise Exception(f"{task}-{mode} has 0 episodes. Remove it from the list in dataset.py")
334
+
335
+ # Select random episode depending on the size of the dataset.
336
+ episodes[task] = np.random.choice(range(n_episodes), min(self.n_demos, n_episodes), False)
337
+
338
+ if self.n_demos > 0:
339
+ self.images = self.cfg['dataset']['images']
340
+ self.cache = False # TODO(mohit): fix caching for multi-task dataset
341
+ self.set(episodes)
342
+
343
+ self._path = None
344
+ self._task = None
345
+
346
+ def __len__(self):
347
+ # Average number of episodes across all tasks
348
+ total_episodes = 0
349
+ for _, episode_ids in self.sample_set.items():
350
+ total_episodes += len(episode_ids)
351
+ avg_episodes = total_episodes # // len(self.sample_set)
352
+ return avg_episodes
353
+
354
+ def __getitem__(self, idx):
355
+ # Choose random task.
356
+ self._task = self.tasks[idx % len(self.tasks)] # np.random.choice(self.tasks)
357
+ self._path = os.path.join(self.root_path, f'{self._task}')
358
+
359
+ # Choose random episode.
360
+ if len(self.sample_set[self._task]) > 0:
361
+ episode_id = np.random.choice(self.sample_set[self._task])
362
+ else:
363
+ episode_id = np.random.choice(range(self.n_episodes[self._task]))
364
+
365
+ res = self.load(episode_id, self.images, self.cache)
366
+ if res is None:
367
+ print("failed in get item", episode_id, self._task, self._path)
368
+ print("Exception:", str(traceback.format_exc()))
369
+
370
+ return self[np.random.randint(len(self))] #
371
+
372
+ episode, _ = res
373
+
374
+ # Is the task sequential like stack-block-pyramid-seq?
375
+ is_sequential_task = '-seq' in self._path.split("/")[-1]
376
+
377
+ # Return observation action pair (and goal) from episode.
378
+ if len(episode) > 1:
379
+ i = np.random.choice(range(len(episode)-1))
380
+ g = i+1 if is_sequential_task else -1
381
+ sample, goal = episode[i], episode[g]
382
+ else:
383
+ sample, goal = episode[0], episode[0]
384
+
385
+ # Process sample
386
+ sample = self.process_sample(sample, augment=self.augment)
387
+ goal = self.process_goal(goal, perturb_params=sample['perturb_params'])
388
+
389
+ return sample, goal
390
+
391
+ def add(self, seed, episode):
392
+ raise Exception("Adding tasks not supported with multi-task dataset")
393
+
394
+ def load(self, episode_id, images=True, cache=False):
395
+ # if self.attr_train_task is None or self.mode in ['val', 'test']:
396
+ # self._task = np.random.choice(self.tasks)
397
+ # else:
398
+ # all_other_tasks = list(self.tasks)
399
+ # all_other_tasks.remove(self.attr_train_task)
400
+ # all_tasks = [self.attr_train_task] + all_other_tasks # add seen task in the front
401
+
402
+ # # 50% chance of sampling the main seen task and 50% chance of sampling any other seen-unseen task
403
+ # mult_attr_seen_sample_prob = 0.5
404
+ # sampling_probs = [(1-mult_attr_seen_sample_prob) / (len(all_tasks)-1)] * len(all_tasks)
405
+ # sampling_probs[0] = mult_attr_seen_sample_prob
406
+
407
+ # self._task = np.random.choice(all_tasks, p=sampling_probs)
408
+
409
+ self._path = os.path.join(self.root_path, f'{self._task}-{self.mode}')
410
+ return super().load(episode_id, images, cache)
411
+
412
+ def get_curr_task(self):
413
+ return self._task
414
+
415
+
416
+ MULTI_TASKS = {
417
+ # new expeeriments
418
+ 'multi-gpt-test': {
419
+ 'train': ['align-box-corner', 'rainbow-stack'],
420
+ 'val': ['align-box-corner', 'rainbow-stack'],
421
+ 'test': ['align-box-corner', 'rainbow-stack']
422
+ },
423
+
424
+ # all tasks
425
+ 'multi-all': {
426
+ 'train': [
427
+ 'align-box-corner',
428
+ 'assembling-kits',
429
+ 'block-insertion',
430
+ 'manipulating-rope',
431
+ 'packing-boxes',
432
+ 'palletizing-boxes',
433
+ 'place-red-in-green',
434
+ 'stack-block-pyramid',
435
+ 'sweeping-piles',
436
+ 'towers-of-hanoi',
437
+ 'align-rope',
438
+ 'assembling-kits-seq-unseen-colors',
439
+ 'packing-boxes-pairs-unseen-colors',
440
+ 'packing-shapes',
441
+ 'packing-unseen-google-objects-seq',
442
+ 'packing-unseen-google-objects-group',
443
+ 'put-block-in-bowl-unseen-colors',
444
+ 'stack-block-pyramid-seq-unseen-colors',
445
+ 'separating-piles-unseen-colors',
446
+ 'towers-of-hanoi-seq-unseen-colors',
447
+ ],
448
+ 'val': [
449
+ 'align-box-corner',
450
+ 'assembling-kits',
451
+ 'block-insertion',
452
+ 'manipulating-rope',
453
+ 'packing-boxes',
454
+ 'palletizing-boxes',
455
+ 'place-red-in-green',
456
+ 'stack-block-pyramid',
457
+ 'sweeping-piles',
458
+ 'towers-of-hanoi',
459
+ 'align-rope',
460
+ 'assembling-kits-seq-seen-colors',
461
+ 'assembling-kits-seq-unseen-colors',
462
+ 'packing-boxes-pairs-seen-colors',
463
+ 'packing-boxes-pairs-unseen-colors',
464
+ 'packing-shapes',
465
+ 'packing-seen-google-objects-seq',
466
+ 'packing-unseen-google-objects-seq',
467
+ 'packing-seen-google-objects-group',
468
+ 'packing-unseen-google-objects-group',
469
+ 'put-block-in-bowl-seen-colors',
470
+ 'put-block-in-bowl-unseen-colors',
471
+ 'stack-block-pyramid-seq-seen-colors',
472
+ 'stack-block-pyramid-seq-unseen-colors',
473
+ 'separating-piles-seen-colors',
474
+ 'separating-piles-unseen-colors',
475
+ 'towers-of-hanoi-seq-seen-colors',
476
+ 'towers-of-hanoi-seq-unseen-colors',
477
+ ],
478
+ 'test': [
479
+ 'align-box-corner',
480
+ 'assembling-kits',
481
+ 'block-insertion',
482
+ 'manipulating-rope',
483
+ 'packing-boxes',
484
+ 'palletizing-boxes',
485
+ 'place-red-in-green',
486
+ 'stack-block-pyramid',
487
+ 'sweeping-piles',
488
+ 'towers-of-hanoi',
489
+ 'align-rope',
490
+ 'assembling-kits-seq-seen-colors',
491
+ 'assembling-kits-seq-unseen-colors',
492
+ 'packing-boxes-pairs-seen-colors',
493
+ 'packing-boxes-pairs-unseen-colors',
494
+ 'packing-shapes',
495
+ 'packing-seen-google-objects-seq',
496
+ 'packing-unseen-google-objects-seq',
497
+ 'packing-seen-google-objects-group',
498
+ 'packing-unseen-google-objects-group',
499
+ 'put-block-in-bowl-seen-colors',
500
+ 'put-block-in-bowl-unseen-colors',
501
+ 'stack-block-pyramid-seq-seen-colors',
502
+ 'stack-block-pyramid-seq-unseen-colors',
503
+ 'separating-piles-seen-colors',
504
+ 'separating-piles-unseen-colors',
505
+ 'towers-of-hanoi-seq-seen-colors',
506
+ 'towers-of-hanoi-seq-unseen-colors',
507
+ ],
508
+ },
509
+
510
+ # demo-conditioned tasks
511
+ 'multi-demo-conditioned': {
512
+ 'train': [
513
+ 'align-box-corner',
514
+ 'assembling-kits',
515
+ 'block-insertion',
516
+ 'manipulating-rope',
517
+ 'packing-boxes',
518
+ 'palletizing-boxes',
519
+ 'place-red-in-green',
520
+ 'stack-block-pyramid',
521
+ 'sweeping-piles',
522
+ 'towers-of-hanoi',
523
+ ],
524
+ 'val': [
525
+ 'align-box-corner',
526
+ 'assembling-kits',
527
+ 'block-insertion',
528
+ 'manipulating-rope',
529
+ 'packing-boxes',
530
+ 'palletizing-boxes',
531
+ 'place-red-in-green',
532
+ 'stack-block-pyramid',
533
+ 'sweeping-piles',
534
+ 'towers-of-hanoi',
535
+ ],
536
+ 'test': [
537
+ 'align-box-corner',
538
+ 'assembling-kits',
539
+ 'block-insertion',
540
+ 'manipulating-rope',
541
+ 'packing-boxes',
542
+ 'palletizing-boxes',
543
+ 'place-red-in-green',
544
+ 'stack-block-pyramid',
545
+ 'sweeping-piles',
546
+ 'towers-of-hanoi',
547
+ ],
548
+ },
549
+
550
+ # goal-conditioned tasks
551
+ 'multi-language-conditioned': {
552
+ 'train': [
553
+ 'align-rope',
554
+ 'assembling-kits-seq-unseen-colors', # unseen here refers to training only seen splits to be consitent with single-task setting
555
+ 'packing-boxes-pairs-unseen-colors',
556
+ 'packing-shapes',
557
+ 'packing-unseen-google-objects-seq',
558
+ 'packing-unseen-google-objects-group',
559
+ 'put-block-in-bowl-unseen-colors',
560
+ 'stack-block-pyramid-seq-unseen-colors',
561
+ 'separating-piles-unseen-colors',
562
+ 'towers-of-hanoi-seq-unseen-colors',
563
+ ],
564
+ 'val': [
565
+ 'align-rope',
566
+ 'assembling-kits-seq-seen-colors',
567
+ 'assembling-kits-seq-unseen-colors',
568
+ 'packing-boxes-pairs-seen-colors',
569
+ 'packing-boxes-pairs-unseen-colors',
570
+ 'packing-shapes',
571
+ 'packing-seen-google-objects-seq',
572
+ 'packing-unseen-google-objects-seq',
573
+ 'packing-seen-google-objects-group',
574
+ 'packing-unseen-google-objects-group',
575
+ 'put-block-in-bowl-seen-colors',
576
+ 'put-block-in-bowl-unseen-colors',
577
+ 'stack-block-pyramid-seq-seen-colors',
578
+ 'stack-block-pyramid-seq-unseen-colors',
579
+ 'separating-piles-seen-colors',
580
+ 'separating-piles-unseen-colors',
581
+ 'towers-of-hanoi-seq-seen-colors',
582
+ 'towers-of-hanoi-seq-unseen-colors',
583
+ ],
584
+ 'test': [
585
+ 'align-rope',
586
+ 'assembling-kits-seq-seen-colors',
587
+ 'assembling-kits-seq-unseen-colors',
588
+ 'packing-boxes-pairs-seen-colors',
589
+ 'packing-boxes-pairs-unseen-colors',
590
+ 'packing-shapes',
591
+ 'packing-seen-google-objects-seq',
592
+ 'packing-unseen-google-objects-seq',
593
+ 'packing-seen-google-objects-group',
594
+ 'packing-unseen-google-objects-group',
595
+ 'put-block-in-bowl-seen-colors',
596
+ 'put-block-in-bowl-unseen-colors',
597
+ 'stack-block-pyramid-seq-seen-colors',
598
+ 'stack-block-pyramid-seq-unseen-colors',
599
+ 'separating-piles-seen-colors',
600
+ 'separating-piles-unseen-colors',
601
+ 'towers-of-hanoi-seq-seen-colors',
602
+ 'towers-of-hanoi-seq-unseen-colors',
603
+ ],
604
+ },
605
+
606
+
607
+ ##### multi-attr tasks
608
+ 'multi-attr-align-rope': {
609
+ 'train': [
610
+ 'assembling-kits-seq-full',
611
+ 'packing-boxes-pairs-full',
612
+ 'packing-shapes',
613
+ 'packing-seen-google-objects-seq',
614
+ 'packing-seen-google-objects-group',
615
+ 'put-block-in-bowl-full',
616
+ 'stack-block-pyramid-seq-full',
617
+ 'separating-piles-full',
618
+ 'towers-of-hanoi-seq-full',
619
+ ],
620
+ 'val': [
621
+ 'align-rope',
622
+ ],
623
+ 'test': [
624
+ 'align-rope',
625
+ ],
626
+ 'attr_train_task': None,
627
+ },
628
+
629
+ 'multi-attr-packing-shapes': {
630
+ 'train': [
631
+ 'align-rope',
632
+ 'assembling-kits-seq-full',
633
+ 'packing-boxes-pairs-full',
634
+ 'packing-seen-google-objects-seq',
635
+ 'packing-seen-google-objects-group',
636
+ 'put-block-in-bowl-full',
637
+ 'stack-block-pyramid-seq-full',
638
+ 'separating-piles-full',
639
+ 'towers-of-hanoi-seq-full',
640
+ ],
641
+ 'val': [
642
+ 'packing-shapes',
643
+ ],
644
+ 'test': [
645
+ 'packing-shapes',
646
+ ],
647
+ 'attr_train_task': None,
648
+ },
649
+
650
+ 'multi-attr-assembling-kits-seq-unseen-colors': {
651
+ 'train': [
652
+ 'align-rope',
653
+ 'assembling-kits-seq-seen-colors', # seen only
654
+ 'packing-boxes-pairs-full',
655
+ 'packing-shapes',
656
+ 'packing-seen-google-objects-seq',
657
+ 'packing-seen-google-objects-group',
658
+ 'put-block-in-bowl-full',
659
+ 'stack-block-pyramid-seq-full',
660
+ 'separating-piles-full',
661
+ 'towers-of-hanoi-seq-full',
662
+ ],
663
+ 'val': [
664
+ 'assembling-kits-seq-unseen-colors',
665
+ ],
666
+ 'test': [
667
+ 'assembling-kits-seq-unseen-colors',
668
+ ],
669
+ 'attr_train_task': 'assembling-kits-seq-seen-colors',
670
+ },
671
+
672
+ 'multi-attr-packing-boxes-pairs-unseen-colors': {
673
+ 'train': [
674
+ 'align-rope',
675
+ 'assembling-kits-seq-full',
676
+ 'packing-boxes-pairs-seen-colors', # seen only
677
+ 'packing-shapes',
678
+ 'packing-seen-google-objects-seq',
679
+ 'packing-seen-google-objects-group',
680
+ 'put-block-in-bowl-full',
681
+ 'stack-block-pyramid-seq-full',
682
+ 'separating-piles-full',
683
+ 'towers-of-hanoi-seq-full',
684
+ ],
685
+ 'val': [
686
+ 'packing-boxes-pairs-unseen-colors',
687
+ ],
688
+ 'test': [
689
+ 'packing-boxes-pairs-unseen-colors',
690
+ ],
691
+ 'attr_train_task': 'packing-boxes-pairs-seen-colors',
692
+ },
693
+
694
+ 'multi-attr-packing-unseen-google-objects-seq': {
695
+ 'train': [
696
+ 'align-rope',
697
+ 'assembling-kits-seq-full',
698
+ 'packing-boxes-pairs-full',
699
+ 'packing-shapes',
700
+ 'packing-seen-google-objects-group',
701
+ 'put-block-in-bowl-full',
702
+ 'stack-block-pyramid-seq-full',
703
+ 'separating-piles-full',
704
+ 'towers-of-hanoi-seq-full',
705
+ ],
706
+ 'val': [
707
+ 'packing-unseen-google-objects-seq',
708
+ ],
709
+ 'test': [
710
+ 'packing-unseen-google-objects-seq',
711
+ ],
712
+ 'attr_train_task': 'packing-seen-google-objects-group',
713
+ },
714
+
715
+ 'multi-attr-packing-unseen-google-objects-group': {
716
+ 'train': [
717
+ 'align-rope',
718
+ 'assembling-kits-seq-full',
719
+ 'packing-boxes-pairs-full',
720
+ 'packing-shapes',
721
+ 'packing-seen-google-objects-seq',
722
+ 'put-block-in-bowl-full',
723
+ 'stack-block-pyramid-seq-full',
724
+ 'separating-piles-full',
725
+ 'towers-of-hanoi-seq-full',
726
+ ],
727
+ 'val': [
728
+ 'packing-unseen-google-objects-group',
729
+ ],
730
+ 'test': [
731
+ 'packing-unseen-google-objects-group',
732
+ ],
733
+ 'attr_train_task': 'packing-seen-google-objects-seq',
734
+ },
735
+
736
+ 'multi-attr-put-block-in-bowl-unseen-colors': {
737
+ 'train': [
738
+ 'align-rope',
739
+ 'assembling-kits-seq-full',
740
+ 'packing-boxes-pairs-full',
741
+ 'packing-shapes',
742
+ 'packing-seen-google-objects-seq',
743
+ 'packing-seen-google-objects-group',
744
+ 'put-block-in-bowl-seen-colors', # seen only
745
+ 'stack-block-pyramid-seq-full',
746
+ 'separating-piles-full',
747
+ 'towers-of-hanoi-seq-full',
748
+ ],
749
+ 'val': [
750
+ 'put-block-in-bowl-unseen-colors',
751
+ ],
752
+ 'test': [
753
+ 'put-block-in-bowl-unseen-colors',
754
+ ],
755
+ 'attr_train_task': 'put-block-in-bowl-seen-colors',
756
+ },
757
+
758
+ 'multi-attr-stack-block-pyramid-seq-unseen-colors': {
759
+ 'train': [
760
+ 'align-rope',
761
+ 'assembling-kits-seq-full',
762
+ 'packing-boxes-pairs-full',
763
+ 'packing-shapes',
764
+ 'packing-seen-google-objects-seq',
765
+ 'packing-seen-google-objects-group',
766
+ 'put-block-in-bowl-full',
767
+ 'stack-block-pyramid-seq-seen-colors', # seen only
768
+ 'separating-piles-full',
769
+ 'towers-of-hanoi-seq-full',
770
+ ],
771
+ 'val': [
772
+ 'stack-block-pyramid-seq-unseen-colors',
773
+ ],
774
+ 'test': [
775
+ 'stack-block-pyramid-seq-unseen-colors',
776
+ ],
777
+ 'attr_train_task': 'stack-block-pyramid-seq-seen-colors',
778
+ },
779
+
780
+ 'multi-attr-separating-piles-unseen-colors': {
781
+ 'train': [
782
+ 'align-rope',
783
+ 'assembling-kits-seq-full',
784
+ 'packing-boxes-pairs-full',
785
+ 'packing-shapes',
786
+ 'packing-seen-google-objects-seq',
787
+ 'packing-seen-google-objects-group',
788
+ 'put-block-in-bowl-full',
789
+ 'stack-block-pyramid-seq-full',
790
+ 'separating-piles-seen-colors', # seen only
791
+ 'towers-of-hanoi-seq-full',
792
+ ],
793
+ 'val': [
794
+ 'separating-piles-unseen-colors',
795
+ ],
796
+ 'test': [
797
+ 'separating-piles-unseen-colors',
798
+ ],
799
+ 'attr_train_task': 'separating-piles-seen-colors',
800
+ },
801
+
802
+ 'multi-attr-towers-of-hanoi-seq-unseen-colors': {
803
+ 'train': [
804
+ 'align-rope',
805
+ 'assembling-kits-seq-full',
806
+ 'packing-boxes-pairs-full',
807
+ 'packing-shapes',
808
+ 'packing-seen-google-objects-seq',
809
+ 'packing-seen-google-objects-group',
810
+ 'put-block-in-bowl-full',
811
+ 'stack-block-pyramid-seq-full',
812
+ 'separating-piles-full',
813
+ 'towers-of-hanoi-seq-seen-colors', # seen only
814
+ ],
815
+ 'val': [
816
+ 'towers-of-hanoi-seq-unseen-colors',
817
+ ],
818
+ 'test': [
819
+ 'towers-of-hanoi-seq-unseen-colors',
820
+ ],
821
+ 'attr_train_task': 'towers-of-hanoi-seq-seen-colors',
822
+ },
823
+
824
+ }
825
+
826
+
827
+
828
+ class RavenMultiTaskDatasetBalance(RavensMultiTaskDataset):
829
+ def __init__(self, path, cfg, group='multi-all',
830
+ mode='train', n_demos=100, augment=False, balance_weight=0.1):
831
+ """A multi-task dataset for balancing data."""
832
+ self.root_path = path
833
+ self.mode = mode
834
+ if group not in self.MULTI_TASKS:
835
+ # generate the groups on the fly
836
+ self.tasks = group# .split(" ")
837
+ else:
838
+ self.tasks = self.MULTI_TASKS[group][mode]
839
+
840
+ print("self.tasks:", self.tasks)
841
+ self.attr_train_task = self.MULTI_TASKS[group]['attr_train_task'] if group in self.MULTI_TASKS and 'attr_train_task' in self.MULTI_TASKS[group] else None
842
+
843
+ self.cfg = cfg
844
+ self.sample_set = {}
845
+ self.max_seed = -1
846
+ self.n_episodes = 0
847
+ self.images = self.cfg['dataset']['images']
848
+ self.cache = self.cfg['dataset']['cache']
849
+ self.n_demos = n_demos
850
+ self.augment = augment
851
+
852
+ self.aug_theta_sigma = self.cfg['dataset']['augment']['theta_sigma'] if 'augment' in self.cfg['dataset'] else 60 # legacy code issue: theta_sigma was newly added
853
+ self.pix_size = 0.003125
854
+ self.in_shape = (320, 160, 6)
855
+ self.cam_config = cameras.RealSenseD415.CONFIG
856
+ self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.28]])
857
+
858
+ self.n_episodes = {}
859
+ episodes = {}
860
+
861
+ for task in self.tasks:
862
+ task_path = os.path.join(self.root_path, f'{task}-{mode}')
863
+ action_path = os.path.join(task_path, 'action')
864
+ n_episodes = 0
865
+ if os.path.exists(action_path):
866
+ for fname in sorted(os.listdir(action_path)):
867
+ if '.pkl' in fname:
868
+ n_episodes += 1
869
+ self.n_episodes[task] = n_episodes
870
+
871
+ if n_episodes == 0:
872
+ raise Exception(f"{task}-{mode} has 0 episodes. Remove it from the list in dataset.py")
873
+
874
+ # Select random episode depending on the size of the dataset.
875
+ if task in self.ORIGINAL_NAMES and self.mode == 'train':
876
+ assert self.n_demos < 200 # otherwise, we need to change the code below
877
+ episodes[task] = np.random.choice(range(n_episodes), min(int(self.n_demos*balance_weight), n_episodes), False)
878
+ else:
879
+ episodes[task] = np.random.choice(range(n_episodes), min(self.n_demos, n_episodes), False)
880
+
881
+ if self.n_demos > 0:
882
+ self.images = self.cfg['dataset']['images']
883
+ self.cache = False
884
+ self.set(episodes)
885
+
886
+ self._path = None
887
+ self._task = None
888
+
889
+
890
+
891
+ ORIGINAL_NAMES = [
892
+ # demo conditioned
893
+ 'align-box-corner',
894
+ 'assembling-kits',
895
+ 'assembling-kits-easy',
896
+ 'block-insertion',
897
+ 'block-insertion-easy',
898
+ 'block-insertion-nofixture',
899
+ 'block-insertion-sixdof',
900
+ 'block-insertion-translation',
901
+ 'manipulating-rope',
902
+ 'packing-boxes',
903
+ 'palletizing-boxes',
904
+ 'place-red-in-green',
905
+ 'stack-block-pyramid',
906
+ 'sweeping-piles',
907
+ 'towers-of-hanoi',
908
+ 'gen-task',
909
+ # goal conditioned
910
+ 'align-rope',
911
+ 'assembling-kits-seq',
912
+ 'assembling-kits-seq-seen-colors',
913
+ 'assembling-kits-seq-unseen-colors',
914
+ 'assembling-kits-seq-full',
915
+ 'packing-shapes',
916
+ 'packing-boxes-pairs',
917
+ 'packing-boxes-pairs-seen-colors',
918
+ 'packing-boxes-pairs-unseen-colors',
919
+ 'packing-boxes-pairs-full',
920
+ 'packing-seen-google-objects-seq',
921
+ 'packing-unseen-google-objects-seq',
922
+ 'packing-seen-google-objects-group',
923
+ 'packing-unseen-google-objects-group',
924
+ 'put-block-in-bowl',
925
+ 'put-block-in-bowl-seen-colors',
926
+ 'put-block-in-bowl-unseen-colors',
927
+ 'put-block-in-bowl-full',
928
+ 'stack-block-pyramid-seq',
929
+ 'stack-block-pyramid-seq-seen-colors',
930
+ 'stack-block-pyramid-seq-unseen-colors',
931
+ 'stack-block-pyramid-seq-full',
932
+ 'separating-piles',
933
+ 'separating-piles-seen-colors',
934
+ 'separating-piles-unseen-colors',
935
+ 'separating-piles-full',
936
+ 'towers-of-hanoi-seq',
937
+ 'towers-of-hanoi-seq-seen-colors',
938
+ 'towers-of-hanoi-seq-unseen-colors',
939
+ 'towers-of-hanoi-seq-full',
940
+ ]
cliport/demos.py ADDED
@@ -0,0 +1,117 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Data collection script."""
2
+
3
+ import os
4
+ import hydra
5
+ import numpy as np
6
+ import random
7
+
8
+ from cliport import tasks
9
+ from cliport.dataset import RavensDataset
10
+ from cliport.environments.environment import Environment
11
+ import IPython
12
+ import random
13
+
14
+ @hydra.main(config_path='./cfg', config_name='data')
15
+ def main(cfg):
16
+ # Initialize environment and task.
17
+ env = Environment(
18
+ cfg['assets_root'],
19
+ disp=cfg['disp'],
20
+ shared_memory=cfg['shared_memory'],
21
+ hz=480,
22
+ record_cfg=cfg['record']
23
+ )
24
+
25
+ task = tasks.names[cfg['task']]()
26
+ task.mode = cfg['mode']
27
+ record = cfg['record']['save_video']
28
+ save_data = cfg['save_data']
29
+
30
+ # Initialize scripted oracle agent and dataset.
31
+ agent = task.oracle(env)
32
+ data_path = os.path.join(cfg['data_dir'], "{}-{}".format(cfg['task'], task.mode))
33
+ dataset = RavensDataset(data_path, cfg, n_demos=0, augment=False)
34
+ print(f"Saving to: {data_path}")
35
+ print(f"Mode: {task.mode}")
36
+
37
+ # Train seeds are even and val/test seeds are odd. Test seeds are offset by 10000
38
+ seed = dataset.max_seed
39
+ max_eps = 3 * cfg['n']
40
+
41
+ if seed < 0:
42
+ if task.mode == 'train':
43
+ seed = -2
44
+ elif task.mode == 'val': # NOTE: beware of increasing val set to >100
45
+ seed = -1
46
+ elif task.mode == 'test':
47
+ seed = -1 + 10000
48
+ else:
49
+ raise Exception("Invalid mode. Valid options: train, val, test")
50
+
51
+ if 'regenerate_data' in cfg:
52
+ dataset.n_episodes = 0
53
+
54
+ curr_run_eps = 0
55
+ # Collect training data from oracle demonstrations.
56
+ while dataset.n_episodes < cfg['n'] and curr_run_eps < max_eps:
57
+ # for epi_idx in range(cfg['n']):
58
+ episode, total_reward = [], 0
59
+ seed += 2
60
+
61
+ # Set seeds.
62
+ np.random.seed(seed)
63
+ random.seed(seed)
64
+ print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, cfg['n'], seed))
65
+ try:
66
+ curr_run_eps += 1 # make sure exits the loop
67
+ env.set_task(task)
68
+ obs = env.reset()
69
+ info = env.info
70
+ reward = 0
71
+
72
+ # Unlikely, but a safety check to prevent leaks.
73
+ if task.mode == 'val' and seed > (-1 + 10000):
74
+ raise Exception("!!! Seeds for val set will overlap with the test set !!!")
75
+
76
+ # Start video recording (NOTE: super slow)
77
+ if record:
78
+ env.start_rec(f'{dataset.n_episodes+1:06d}')
79
+
80
+
81
+ # Rollout expert policy
82
+ for _ in range(task.max_steps):
83
+ act = agent.act(obs, info)
84
+ episode.append((obs, act, reward, info))
85
+ lang_goal = info['lang_goal']
86
+ obs, reward, done, info = env.step(act)
87
+ total_reward += reward
88
+ print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}')
89
+ if done:
90
+ break
91
+ if record:
92
+ env.end_rec()
93
+
94
+ except Exception as e:
95
+ from pygments import highlight
96
+ from pygments.lexers import PythonLexer
97
+ from pygments.formatters import TerminalFormatter
98
+ import traceback
99
+
100
+ to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
101
+ print(to_print)
102
+ if record:
103
+ env.end_rec()
104
+ continue
105
+
106
+ episode.append((obs, None, reward, info))
107
+
108
+ # Only save completed demonstrations.
109
+ if save_data and total_reward > 0.99:
110
+ dataset.add(seed, episode)
111
+ if hasattr(env, 'blender_recorder'):
112
+ print("blender pickle saved to ", '{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes))
113
+ env.blender_recorder.save('{}/blender_demo_{}.pkl'.format(data_path, dataset.n_episodes))
114
+
115
+
116
+ if __name__ == '__main__':
117
+ main()
cliport/demos_gpt4.py ADDED
@@ -0,0 +1,357 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 The Ravens Authors.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ """Data collection script."""
17
+
18
+ import os
19
+
20
+ import numpy as np
21
+ import os
22
+ import hydra
23
+ import numpy as np
24
+ import random
25
+
26
+ from cliport import tasks
27
+ from cliport.dataset import RavensDataset
28
+ from cliport.environments.environment import Environment
29
+
30
+ from pygments import highlight
31
+ from pygments.lexers import PythonLexer
32
+ from pygments.formatters import TerminalFormatter
33
+ import re
34
+
35
+ import openai
36
+ import IPython
37
+ import time
38
+ import pybullet as p
39
+ import traceback
40
+ from datetime import datetime
41
+ from pprint import pprint
42
+ import cv2
43
+ import re
44
+ import random
45
+ import json
46
+ from cliport.simgen_utils import (mkdir_if_missing,
47
+ save_text,
48
+ add_to_txt,
49
+ extract_code,
50
+ extract_dict,
51
+ extract_list,
52
+ extract_assets,
53
+ format_dict_prompt,
54
+ sample_list_reference,
55
+ save_stat,
56
+ compute_diversity_score_from_assets)
57
+
58
+
59
+
60
+ openai.api_key = "YOUR_KEY"
61
+ model = "gpt-4"
62
+ NEW_TASK_LIST = []
63
+ full_interaction = ''
64
+
65
+ def generate_feedback(prompt, max_tokens=2048, temperature=0.0, model="gpt-4", assistant_prompt=None, interaction_txt=None):
66
+ """ use GPT-4 API """
67
+ params = {
68
+ "model": model,
69
+ "max_tokens": max_tokens,
70
+ "temperature": temperature,
71
+ "messages": [
72
+ {"role": "user", "content": prompt}],
73
+ }
74
+ if assistant_prompt is not None:
75
+ params["messages"].append({"role": "assistant", "content": assistant_prompt})
76
+
77
+ for retry in range(3):
78
+ try:
79
+ if interaction_txt is not None:
80
+ interaction_txt = add_to_txt(interaction_txt, ">>> Prompt: \n" + prompt, with_print=False)
81
+ res = openai.ChatCompletion.create(**params)["choices"][0]["message"]["content"]
82
+ to_print = highlight(f"{res}", PythonLexer(), TerminalFormatter())
83
+ print(to_print)
84
+ if interaction_txt is not None:
85
+ interaction_txt = add_to_txt(interaction_txt, ">>> Answer: \n" + res, with_print=False)
86
+ return res, interaction_txt
87
+ return res
88
+
89
+ except Exception as e:
90
+ print("failed chat completion", e)
91
+ raise Exception("Failed to generate")
92
+
93
+
94
+ def llm_gen_env(cfg, model_output_dir):
95
+ """
96
+ The LLM running pipeline
97
+ """
98
+ global full_interaction
99
+ start_time = time.time()
100
+ prompt_folder = f"prompts/{cfg['prompt_folder']}"
101
+ task_prompt_text = open(f"{prompt_folder}/cliport_prompt_task.txt").read()
102
+ res, full_interaction = generate_feedback(task_prompt_text, temperature=cfg['gpt_temperature'], interaction_txt=full_interaction)
103
+
104
+ # Extract dictionary for task name, descriptions, and assets
105
+ task_def = extract_dict(res, prefix="new_task")
106
+ exec(task_def, globals())
107
+
108
+ full_interaction = add_to_txt(full_interaction, "================= Task and Asset Design!", with_print=True)
109
+ pprint(new_task)
110
+ save_text(model_output_dir, f'{new_task["task-name"]}_task_def_output', res)
111
+
112
+ # Asset Generation
113
+ if os.path.exists(f"{prompt_folder}/cliport_prompt_asset_template.txt"):
114
+ full_interaction = add_to_txt(full_interaction, "================= Asset Generation!", with_print=True)
115
+ asset_prompt_text = open(f'{prompt_folder}/cliport_prompt_asset_template.txt').read()
116
+ asset_prompt_text = asset_prompt_text.replace("TASK_NAME_TEMPLATE", new_task["task-name"])
117
+ asset_prompt_text = asset_prompt_text.replace("ASSET_STRING_TEMPLATE", str(new_task["assets-used"]))
118
+
119
+ res, full_interaction = generate_feedback(asset_prompt_text, temperature=0, assistant_prompt=res, interaction_txt=full_interaction) # cfg['gpt_temperature']
120
+ save_text(model_output_dir, f'{new_task["task-name"]}_asset_output', res)
121
+ asset_list = extract_assets(res)
122
+ # save_urdf(asset_list)
123
+ else:
124
+ asset_list = {}
125
+
126
+ # API Preview
127
+ if os.path.exists(f"{prompt_folder}/cliport_prompt_api_template.txt"):
128
+ full_interaction = add_to_txt(full_interaction,"================= API Preview!")
129
+ api_prompt_text = open(f'{prompt_folder}/cliport_prompt_api_template.txt').read()
130
+ api_prompt_text = api_prompt_text.replace("TASK_NAME_TEMPLATE", new_task["task-name"])
131
+ res, full_interaction = generate_feedback(api_prompt_text, temperature=0, assistant_prompt=res, interaction_txt=full_interaction) # cfg['gpt_temperature']
132
+
133
+ # Error Preview
134
+ if os.path.exists(f"{prompt_folder}/cliport_prompt_common_errors_template.txt"):
135
+ full_interaction = add_to_txt(full_interaction,"================= Error Book Preview!")
136
+ errorbook_prompt_text = open(f'{prompt_folder}/cliport_prompt_common_errors_template.txt').read()
137
+ errorbook_prompt_text = errorbook_prompt_text.replace("TASK_NAME_TEMPLATE", new_task["task-name"])
138
+ res, full_interaction = generate_feedback(errorbook_prompt_text, temperature=0., assistant_prompt=res, interaction_txt=full_interaction) # cfg['gpt_temperature']
139
+
140
+ # Generate Code
141
+ if os.path.exists(f"{prompt_folder}/cliport_prompt_code_split_template.txt"):
142
+ full_interaction = add_to_txt(full_interaction,"================= Code Generation!")
143
+ code_prompt_text = open(f"{prompt_folder}/cliport_prompt_code_split_template.txt").read()
144
+ code_prompt_text = code_prompt_text.replace("TASK_NAME_TEMPLATE", new_task["task-name"])
145
+ code_prompt_text = code_prompt_text.replace("TASK_STRING_TEMPLATE", str(new_task))
146
+ res, full_interaction = generate_feedback(code_prompt_text, temperature=0., assistant_prompt=res, interaction_txt=full_interaction) # cfg['gpt_temperature']
147
+
148
+ code, task_name = extract_code(res)
149
+
150
+ if len(task_name) == 0:
151
+ print("empty task name:", task_name)
152
+ return None
153
+
154
+ save_text(model_output_dir, task_name + '_code_output', code)
155
+ try:
156
+ exec(code, globals())
157
+ except:
158
+ print(str(traceback.format_exc()))
159
+ return None
160
+
161
+ cfg['task'] = new_task["task-name"]
162
+ print("save all interaction to :", f'{new_task["task-name"]}_full_output')
163
+ save_text(model_output_dir, f'{new_task["task-name"]}_full_output', full_interaction)
164
+ print(f"\n\nLLM generation time: {time.time() - start_time}")
165
+ return task_name, new_task, asset_list, code
166
+
167
+
168
+ @hydra.main(config_path='./cfg', config_name='data')
169
+ def main(cfg):
170
+ global full_interaction
171
+
172
+ # Evaluation Metric
173
+ SYNTAX_PASS_RATE = 0.
174
+ RUNTIME_PASS_RATE = 0.
175
+ ENV_PASS_RATE = 0.
176
+ DIVERSITY_SCORES = 0
177
+
178
+ task_assets = []
179
+ start_time = time.time()
180
+ output_folder = 'output/output_stats'
181
+
182
+ model_time = datetime.now().strftime("%d_%m_%Y_%H:%M:%S")
183
+ model_output_dir = os.path.join(output_folder, cfg['prompt_folder'] + "_" + model_time)
184
+ TOTAL_TRIALS = cfg['trials']
185
+ env_names = []
186
+
187
+ for trial_i in range(TOTAL_TRIALS):
188
+
189
+ # generate
190
+ res = llm_gen_env(cfg, model_output_dir)
191
+ if res is not None:
192
+ SYNTAX_PASS_RATE += 1
193
+ task_name, new_task, asset_list, code = res
194
+ task_assets.append(new_task["assets-used"])
195
+ env_names.append(task_name)
196
+ else:
197
+ env_names.append("")
198
+ print("Syntax Failure")
199
+ continue
200
+
201
+ try:
202
+ env = Environment(
203
+ cfg['assets_root'],
204
+ disp=cfg['disp'],
205
+ shared_memory=cfg['shared_memory'],
206
+ hz=480,
207
+ record_cfg=cfg['record']
208
+ )
209
+
210
+ task = eval(task_name)()
211
+ task.mode = cfg['mode']
212
+ record = cfg['record']['save_video']
213
+ save_data = cfg['save_data']
214
+
215
+ # Initialize scripted oracle agent and dataset.
216
+ agent = task.oracle(env)
217
+ data_path = os.path.join(cfg['data_dir'], "{}-{}".format(cfg['task'], task.mode))
218
+ dataset = RavensDataset(data_path, cfg, n_demos=0, augment=False)
219
+ print(f"Saving to: {data_path}")
220
+ print(f"Mode: {task.mode}")
221
+
222
+ # Train seeds are even and val/test seeds are odd. Test seeds are offset by 10000
223
+ seed = dataset.max_seed
224
+ total_cnt = 0.
225
+ reset_success_cnt = 0.
226
+ env_success_cnt = 0.
227
+
228
+ # Start video recording (NOTE: super slow)
229
+ if record:
230
+ env.start_rec(f'{dataset.n_episodes+1:06d}')
231
+
232
+ # Collect training data from oracle demonstrations.
233
+ # while dataset.n_episodes < cfg['n']:
234
+ while total_cnt < cfg['max_env_run_cnt']:
235
+ total_cnt += 1
236
+ if total_cnt == cfg['max_env_run_cnt'] or total_cnt == cfg['n']:
237
+ if reset_success_cnt == total_cnt - 1:
238
+ RUNTIME_PASS_RATE += 1
239
+ print("Runtime Test Pass!")
240
+
241
+ # the task can actually be completed with oracle
242
+ if env_success_cnt >= total_cnt / 2:
243
+ ENV_PASS_RATE += 1
244
+ print("Environment Test Pass!")
245
+ else:
246
+ print("Bad task design!! Reset!")
247
+
248
+ break
249
+
250
+ episode, total_reward = [], 0
251
+ seed += 2
252
+
253
+ # Set seeds.
254
+ np.random.seed(seed)
255
+ random.seed(seed)
256
+ print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, cfg['n'], seed))
257
+ env.set_task(task)
258
+
259
+ try:
260
+ obs = env.reset()
261
+ except Exception as e:
262
+ print("reset exception:", str(traceback.format_exc()))
263
+ continue
264
+
265
+ info = env.info
266
+ reward = 0
267
+
268
+
269
+ # Rollout expert policy
270
+ for _ in range(task.max_steps):
271
+ act = agent.act(obs, info)
272
+ episode.append((obs, act, reward, info))
273
+ lang_goal = info['lang_goal']
274
+ obs, reward, done, info = env.step(act)
275
+ total_reward += reward
276
+ print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}')
277
+ if done:
278
+ break
279
+
280
+ episode.append((obs, None, reward, info))
281
+
282
+ # End video recording
283
+ if record:
284
+ env.end_rec()
285
+
286
+ # Only save completed demonstrations.
287
+ if save_data and total_reward > 0.99:
288
+ dataset.add(seed, episode)
289
+
290
+ reset_success_cnt += 1
291
+ env_success_cnt += total_reward > 0.99
292
+
293
+ p.disconnect()
294
+
295
+ except:
296
+ to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
297
+ save_text(model_output_dir, task_name + '_error', str(traceback.format_exc()))
298
+
299
+ print("========================================================")
300
+ print("Exception:", to_print)
301
+ p.disconnect()
302
+
303
+ print("=========================================================")
304
+ print(f"SYNTAX_PASS_RATE: {(SYNTAX_PASS_RATE / (trial_i+1)) * 100:.1f}% RUNTIME_PASS_RATE: {(RUNTIME_PASS_RATE / (trial_i+1)) * 100:.1f}% ENV_PASS_RATE: {(ENV_PASS_RATE / (trial_i+1)) * 100:.1f}%")
305
+ print("=========================================================")
306
+
307
+ prompt_folder = f"prompts/{cfg['prompt_folder']}"
308
+ if os.path.exists(f"{prompt_folder}/cliport_prompt_task_reflection.txt") and env_success_cnt >= 1:
309
+ # only consider successful task
310
+ full_interaction = add_to_txt(full_interaction,"================= Code Reflect!")
311
+
312
+ base_task_path = os.path.join("prompts/data", 'base_tasks.json')
313
+ base_tasks = json.load(open(base_task_path))
314
+
315
+ # append current new task
316
+ for task in NEW_TASK_LIST:
317
+ base_tasks[task["task-name"].replace("-", "_")] = str(task)
318
+
319
+ task_descriptions_replacement_str = format_dict_prompt(base_tasks, -1)
320
+ code_reflection_prompt_text = open(f"{prompt_folder}/cliport_prompt_task_reflection.txt").read()
321
+ code_reflection_prompt_text = code_reflection_prompt_text.replace("CURRENT_TASK_NAME_TEMPLATE", str(task_descriptions_replacement_str))
322
+ code_reflection_prompt_text = code_reflection_prompt_text.replace("TASK_STRING_TEMPLATE", str(new_task))
323
+ res, full_interaction = generate_feedback(code_reflection_prompt_text, temperature=0., interaction_txt=full_interaction) # cfg['gpt_temperature']
324
+ reflection_def_cmd = extract_dict(res, prefix='task_reflection')
325
+ exec(reflection_def_cmd, globals())
326
+ print("save task result:", task_reflection)
327
+
328
+ if task_reflection["add_to_the_task_list"] == 'True':
329
+ NEW_TASK_LIST.append(new_task)
330
+
331
+ if cfg['save_memory']:
332
+ print("actually saving!")
333
+
334
+ # write the python file and append to the task descriptions
335
+ generated_task_code_path = os.path.join(cfg['prompt_data_path'], 'generated_task_codes.json')
336
+ generated_task_codes = json.load(open(generated_task_code_path))
337
+ generated_task_codes.append(new_task["task-name"] + ".py")
338
+ with open('cliport/generated_tasks/' + new_task["task-name"].replace("-","_") + ".py", "w") as fhandle:
339
+ fhandle.write(code)
340
+
341
+ with open(generated_task_code_path, "w") as outfile:
342
+ json.dump(generated_task_codes, outfile, indent=4)
343
+
344
+ generated_task_path = os.path.join(cfg['prompt_data_path'], 'generated_tasks.json')
345
+ generated_tasks = json.load(open(generated_task_path))
346
+ generated_tasks[new_task["task-name"]] = new_task
347
+
348
+ with open(generated_task_path, "w") as outfile:
349
+ json.dump(generated_tasks, outfile, indent=4)
350
+
351
+ print("task_assets:", task_assets)
352
+ DIVERSITY_SCORE = compute_diversity_score_from_assets(task_assets)
353
+ save_stat(cfg, model_output_dir, env_names, SYNTAX_PASS_RATE / TOTAL_TRIALS, RUNTIME_PASS_RATE / TOTAL_TRIALS, ENV_PASS_RATE / TOTAL_TRIALS, DIVERSITY_SCORE)
354
+ print(f"Total {len(NEW_TASK_LIST)} New Added Tasks:", NEW_TASK_LIST)
355
+
356
+ if __name__ == '__main__':
357
+ main()
cliport/environments/__init__.py ADDED
File without changes
cliport/environments/assets/bags/bl_sphere_bag_basic_000.mtl ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Blender MTL File: 'None'
2
+ # Material Count: 1
3
+
4
+ newmtl CustomColor.001
5
+ Ns 323.999994
6
+ Ka 1.000000 1.000000 1.000000
7
+ Kd 0.000000 0.000000 1.000000
8
+ Ks 0.500000 0.500000 0.500000
9
+ Ke 0.000000 0.000000 0.000000
10
+ Ni 1.000000
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+ d 1.000000
12
+ illum 2
cliport/environments/assets/bags/bl_sphere_bag_basic_000.obj ADDED
@@ -0,0 +1,1587 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Blender v2.82 (sub 7) OBJ File: ''
2
+ # www.blender.org
3
+ mtllib bl_sphere_bag_basic_000.mtl
4
+ o Sphere
5
+ v -4.000000 2.565686 -0.565685
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+ # www.blender.org
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+ vn -0.1374 -0.8810 -0.4528
1094
+ vn -0.0865 -0.4696 -0.8786
1095
+ vn -0.0759 -0.6326 -0.7708
1096
+ vn -0.0624 -0.7715 -0.6332
1097
+ vn -0.0865 0.4696 -0.8786
1098
+ vn -0.0464 -0.8810 -0.4709
1099
+ vn -0.0938 0.2890 -0.9527
1100
+ vn -0.0286 -0.9565 -0.2902
1101
+ vn -0.0976 0.0976 -0.9904
1102
+ vn -0.0097 -0.9951 -0.0980
1103
+ vn -0.0976 -0.0976 -0.9904
1104
+ vn -0.0938 -0.2890 -0.9527
1105
+ usemtl None
1106
+ s off
1107
+ f 350/1/1 349/2/1 12/3/1 13/4/1
1108
+ f 351/5/2 350/1/2 13/4/2 14/6/2
1109
+ f 6/7/3 351/5/3 14/6/3 15/8/3
1110
+ f 2/9/4 1/10/4 7/11/4 8/12/4
1111
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1112
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1113
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1114
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1115
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1116
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1117
+ f 349/2/11 5/22/11 11/23/11 12/3/11
1118
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1119
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1120
+ f 14/6/14 13/4/14 24/26/14 25/27/14
1121
+ f 15/8/15 14/6/15 25/27/15 26/28/15
1122
+ f 8/12/16 7/11/16 18/29/16 19/30/16
1123
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1124
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1125
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1126
+ f 10/20/20 9/16/20 20/32/20 21/34/20
1127
+ f 150/35/21 17/18/21 28/33/21
1128
+ f 11/23/22 10/20/22 21/34/22 22/24/22
1129
+ f 19/30/23 18/29/23 29/36/23 30/37/23
1130
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1131
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1132
+ f 28/33/26 27/31/26 38/39/26 39/41/26
1133
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1134
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1135
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1136
+ f 23/25/30 22/24/30 33/44/30 34/45/30
1137
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1138
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1139
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1140
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1141
+ f 35/46/35 34/45/35 45/49/35 46/50/35
1142
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1143
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1144
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1145
+ f 38/39/39 37/38/39 48/52/39 49/55/39
1146
+ f 31/40/40 30/37/40 41/54/40 42/56/40
1147
+ f 39/41/41 38/39/41 49/55/41 50/57/41
1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
+ f 45/49/51 44/48/51 55/67/51 56/68/51
1158
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1159
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1160
+ f 48/52/54 47/51/54 58/70/54 59/60/54
1161
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1162
+ f 57/69/56 56/68/56 67/72/56 68/73/56
1163
+ f 58/70/57 57/69/57 68/73/57 69/74/57
1164
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1165
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1166
+ f 60/61/60 59/60/60 70/75/60 71/78/60
1167
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1168
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1169
+ f 54/65/63 53/63/63 64/79/63 65/81/63
1170
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1171
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1172
+ f 56/68/66 55/67/66 66/83/66 67/72/66
1173
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1174
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1175
+ f 65/81/69 64/79/69 75/85/69 76/88/69
1176
+ f 150/89/70 72/80/70 83/87/70
1177
+ f 66/83/71 65/81/71 76/88/71 77/90/71
1178
+ f 67/72/72 66/83/72 77/90/72 78/91/72
1179
+ f 68/73/73 67/72/73 78/91/73 79/92/73
1180
+ f 69/74/74 68/73/74 79/92/74 80/93/74
1181
+ f 70/75/75 69/74/75 80/93/75 81/94/75
1182
+ f 63/77/76 62/76/76 73/95/76 74/84/76
1183
+ f 71/78/77 70/75/77 81/94/77 82/86/77
1184
+ f 79/92/78 78/91/78 89/96/78 90/97/78
1185
+ f 80/93/79 79/92/79 90/97/79 91/98/79
1186
+ f 81/94/80 80/93/80 91/98/80 92/99/80
1187
+ f 74/84/81 73/95/81 84/100/81 85/101/81
1188
+ f 82/86/82 81/94/82 92/99/82 93/102/82
1189
+ f 75/85/83 74/84/83 85/101/83 86/103/83
1190
+ f 83/87/84 82/86/84 93/102/84 94/104/84
1191
+ f 76/88/85 75/85/85 86/103/85 87/105/85
1192
+ f 150/106/86 83/87/86 94/104/86
1193
+ f 77/90/87 76/88/87 87/105/87 88/107/87
1194
+ f 78/91/88 77/90/88 88/107/88 89/96/88
1195
+ f 94/104/89 93/102/89 104/108/89 105/109/89
1196
+ f 87/105/90 86/103/90 97/110/90 98/111/90
1197
+ f 150/112/91 94/104/91 105/109/91
1198
+ f 88/107/92 87/105/92 98/111/92 99/113/92
1199
+ f 89/96/93 88/107/93 99/113/93 100/114/93
1200
+ f 90/97/94 89/96/94 100/114/94 101/115/94
1201
+ f 91/98/95 90/97/95 101/115/95 102/116/95
1202
+ f 92/99/96 91/98/96 102/116/96 103/117/96
1203
+ f 85/101/97 84/100/97 95/118/97 96/119/97
1204
+ f 93/102/98 92/99/98 103/117/98 104/108/98
1205
+ f 86/103/99 85/101/99 96/119/99 97/110/99
1206
+ f 102/116/100 101/115/100 112/120/100 113/121/100
1207
+ f 103/117/101 102/116/101 113/121/101 114/122/101
1208
+ f 96/119/102 95/118/102 106/123/102 107/124/102
1209
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1210
+ f 97/110/104 96/119/104 107/124/104 108/126/104
1211
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1212
+ f 98/111/106 97/110/106 108/126/106 109/128/106
1213
+ f 150/129/107 105/109/107 116/127/107
1214
+ f 99/113/108 98/111/108 109/128/108 110/130/108
1215
+ f 100/114/109 99/113/109 110/130/109 111/131/109
1216
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1217
+ f 150/132/111 116/127/111 127/133/111
1218
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1219
+ f 111/131/113 110/130/113 121/135/113 122/136/113
1220
+ f 112/120/114 111/131/114 122/136/114 123/137/114
1221
+ f 113/121/115 112/120/115 123/137/115 124/138/115
1222
+ f 114/122/116 113/121/116 124/138/116 125/139/116
1223
+ f 107/124/117 106/123/117 117/140/117 118/141/117
1224
+ f 115/125/118 114/122/118 125/139/118 126/142/118
1225
+ f 108/126/119 107/124/119 118/141/119 119/143/119
1226
+ f 116/127/120 115/125/120 126/142/120 127/133/120
1227
+ f 109/128/121 108/126/121 119/143/121 120/134/121
1228
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1229
+ f 118/141/123 117/140/123 128/146/123 129/147/123
1230
+ f 126/142/124 125/139/124 136/145/124 137/148/124
1231
+ f 119/143/125 118/141/125 129/147/125 130/149/125
1232
+ f 127/133/126 126/142/126 137/148/126 138/150/126
1233
+ f 120/134/127 119/143/127 130/149/127 131/151/127
1234
+ f 150/152/128 127/133/128 138/150/128
1235
+ f 121/135/129 120/134/129 131/151/129 132/153/129
1236
+ f 122/136/130 121/135/130 132/153/130 133/154/130
1237
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1238
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1239
+ f 132/153/133 131/151/133 142/156/133 143/157/133
1240
+ f 133/154/134 132/153/134 143/157/134 144/158/134
1241
+ f 134/155/135 133/154/135 144/158/135 145/159/135
1242
+ f 135/144/136 134/155/136 145/159/136 146/160/136
1243
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1244
+ f 129/147/138 128/146/138 139/162/138 140/163/138
1245
+ f 137/148/139 136/145/139 147/161/139 148/164/139
1246
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1247
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1248
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1249
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1250
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1251
+ f 140/163/145 139/162/145 151/170/145 152/171/145
1252
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1253
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1254
+ f 149/166/148 148/164/148 160/172/148 161/174/148
1255
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1256
+ f 150/176/150 149/166/150 161/174/150
1257
+ f 143/157/151 142/156/151 154/175/151 155/177/151
1258
+ f 144/158/152 143/157/152 155/177/152 156/178/152
1259
+ f 145/159/153 144/158/153 156/178/153 157/179/153
1260
+ f 146/160/154 145/159/154 157/179/154 158/168/154
1261
+ f 156/178/155 155/177/155 166/180/155 167/181/155
1262
+ f 157/179/156 156/178/156 167/181/156 168/182/156
1263
+ f 158/168/157 157/179/157 168/182/157 169/183/157
1264
+ f 159/169/158 158/168/158 169/183/158 170/184/158
1265
+ f 152/171/159 151/170/159 162/185/159 163/186/159
1266
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1267
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1268
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1269
+ f 154/175/163 153/173/163 164/188/163 165/190/163
1270
+ f 150/191/164 161/174/164 172/189/164
1271
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1272
+ f 163/186/166 162/185/166 173/192/166 174/193/166
1273
+ f 171/187/167 170/184/167 181/194/167 182/195/167
1274
+ f 164/188/168 163/186/168 174/193/168 175/196/168
1275
+ f 172/189/169 171/187/169 182/195/169 183/197/169
1276
+ f 165/190/170 164/188/170 175/196/170 176/198/170
1277
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1278
+ f 166/180/172 165/190/172 176/198/172 177/200/172
1279
+ f 167/181/173 166/180/173 177/200/173 178/201/173
1280
+ f 168/182/174 167/181/174 178/201/174 179/202/174
1281
+ f 169/183/175 168/182/175 179/202/175 180/203/175
1282
+ f 170/184/176 169/183/176 180/203/176 181/194/176
1283
+ f 178/201/177 177/200/177 188/204/177 189/205/177
1284
+ f 179/202/178 178/201/178 189/205/178 190/206/178
1285
+ f 180/203/179 179/202/179 190/206/179 191/207/179
1286
+ f 181/194/180 180/203/180 191/207/180 192/208/180
1287
+ f 174/193/181 173/192/181 184/209/181 185/210/181
1288
+ f 182/195/182 181/194/182 192/208/182 193/211/182
1289
+ f 175/196/183 174/193/183 185/210/183 186/212/183
1290
+ f 183/197/184 182/195/184 193/211/184 194/213/184
1291
+ f 176/198/185 175/196/185 186/212/185 187/214/185
1292
+ f 150/215/186 183/197/186 194/213/186
1293
+ f 177/200/187 176/198/187 187/214/187 188/204/187
1294
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1295
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1296
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1297
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1298
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1299
+ f 188/204/193 187/214/193 198/221/193 199/223/193
1300
+ f 189/205/194 188/204/194 199/223/194 200/224/194
1301
+ f 190/206/195 189/205/195 200/224/195 201/225/195
1302
+ f 191/207/196 190/206/196 201/225/196 202/226/196
1303
+ f 192/208/197 191/207/197 202/226/197 203/216/197
1304
+ f 185/210/198 184/209/198 195/227/198 196/218/198
1305
+ f 201/225/199 200/224/199 211/228/199 212/229/199
1306
+ f 202/226/200 201/225/200 212/229/200 213/230/200
1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
+ f 199/223/208 198/221/208 209/237/208 210/239/208
1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
+ f 207/233/218 206/232/218 217/250/218 218/251/218
1325
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1326
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1327
+ f 224/248/221 223/247/221 234/252/221 235/253/221
1328
+ f 225/249/222 224/248/222 235/253/222 236/254/222
1329
+ f 218/251/223 217/250/223 228/255/223 229/256/223
1330
+ f 226/240/224 225/249/224 236/254/224 237/257/224
1331
+ f 219/242/225 218/251/225 229/256/225 230/258/225
1332
+ f 227/241/226 226/240/226 237/257/226 238/259/226
1333
+ f 220/243/227 219/242/227 230/258/227 231/260/227
1334
+ f 150/261/228 227/241/228 238/259/228
1335
+ f 221/245/229 220/243/229 231/260/229 232/262/229
1336
+ f 222/246/230 221/245/230 232/262/230 233/263/230
1337
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1338
+ f 231/260/232 230/258/232 241/264/232 242/265/232
1339
+ f 150/266/233 238/259/233 249/267/233
1340
+ f 232/262/234 231/260/234 242/265/234 243/268/234
1341
+ f 233/263/235 232/262/235 243/268/235 244/269/235
1342
+ f 234/252/236 233/263/236 244/269/236 245/270/236
1343
+ f 235/253/237 234/252/237 245/270/237 246/271/237
1344
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1345
+ f 229/256/239 228/255/239 239/273/239 240/274/239
1346
+ f 237/257/240 236/254/240 247/272/240 248/275/240
1347
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1348
+ f 238/259/242 237/257/242 248/275/242 249/267/242
1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
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1378
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1379
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1380
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1381
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1382
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1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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cliport/environments/assets/bags/bl_sphere_bag_rad_1.0_zthresh_0.8_numV_385_top_ring.txt ADDED
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cliport/environments/assets/ball/ball-template.urdf ADDED
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cliport/environments/assets/ball/ball-template_COLOR.urdf ADDED
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <sphere radius="0.03"/>
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+ </geometry>
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+ </collision>
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+ </link>
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+ </robot>
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+
cliport/environments/assets/ball/ball-template_DIM.urdf ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="0.0" ?>
2
+ <robot name="box.urdf">
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+ <link name="baseLink">
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+ <contact>
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+ <lateral_friction value="1.0"/>
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+ <rolling_friction value="0.0001"/>
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+ <inertia_scaling value="3.0"/>
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+ </contact>
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+ <inertial>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <mass value="0.1"/>
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+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <sphere radius="DIM0"/>
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+ </geometry>
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+ <material name="red">
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+ <color rgba="1 0.3412 0.3490 1"/>
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+ </material>
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+ </visual>
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+ <collision>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <sphere radius="DIM0"/>
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+ </geometry>
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+ </collision>
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+ </link>
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+ </robot>
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+
cliport/environments/assets/ball/ball.urdf ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0" encoding="UTF-8"?>
2
+
3
+ <robot name="box.urdf">
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+ <link name="baseLink">
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+ <contact>
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+ <lateral_friction value="1.0"/>
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+ <rolling_friction value="0.0001"/>
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+ <inertia_scaling value="3.0"/>
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+ </contact>
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+ <inertial>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <mass value="0.1"/>
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+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <sphere radius="0.03"/>
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+ </geometry>
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+ <material name="red">
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+ <color rgba="1 0.3412 0.3490 1"/>
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+ </material>
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+ </visual>
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+ <collision>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <sphere radius="0.03"/>
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+ </geometry>
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+ </collision>
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+ </link>
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+ </robot>
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+
cliport/environments/assets/block/block.urdf ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0" encoding="UTF-8"?>
2
+ <robot name="box.urdf">
3
+ <link name="baseLink">
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+ <contact>
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+ <lateral_friction value="1.0"/>
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+ <rolling_friction value="0.0001"/>
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+ <inertia_scaling value="3.0"/>
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+ </contact>
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+ <inertial>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <mass value=".1"/>
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+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <box size="0.04 0.04 0.04"/>
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+ </geometry>
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+ <material name="red">
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+ <color rgba="1 0.3412 0.3490 1"/>
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+ </material>
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+ </visual>
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+ <collision>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <box size="0.04 0.04 0.04"/>
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+ </geometry>
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+ </collision>
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+ </link>
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+ </robot>
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+
cliport/environments/assets/block/block_for_anchors.urdf ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0" encoding="UTF-8"?>
2
+
3
+ <robot name="box.urdf">
4
+ <link name="baseLink">
5
+ <contact>
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+ <lateral_friction value="1.0"/>
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+ <rolling_friction value="0.0001"/>
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+ <inertia_scaling value="3.0"/>
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+ </contact>
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+ <inertial>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <mass value="0.02"/>
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+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <box size="0.04 0.04 0.04"/>
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+ </geometry>
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+ <material name="red">
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+ <color rgba="1 0.3412 0.3490 1"/>
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+ </material>
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+ </visual>
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+ <collision>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <box size="0.04 0.04 0.04"/>
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+ </geometry>
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+ </collision>
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+ </link>
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+ </robot>
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+
cliport/environments/assets/block/small.urdf ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0" encoding="UTF-8"?>
2
+ <robot name="small.urdf">
3
+ <link name="baseLink">
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+ <contact>
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+ <lateral_friction value="0.5"/>
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+ <rolling_friction value="0.0001"/>
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+ <inertia_scaling value="3.0"/>
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+ </contact>
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+ <inertial>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <mass value=".001"/>
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+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
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+ <box size="0.01 0.01 0.01"/>
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+ </geometry>
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+ <material name="red">
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+ <color rgba="1 0.3412 0.3490 1"/>
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+ </material>
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+ </visual>
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+ <collision>
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+ <origin rpy="0 0 0" xyz="0 0 0"/>
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+ <geometry>
26
+ <box size="0.01 0.01 0.01"/>
27
+ </geometry>
28
+ </collision>
29
+ </link>
30
+ </robot>
31
+
cliport/environments/assets/bowl/bowl.urdf ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0" ?>
2
+ <robot name="bowl.urdf">
3
+ <link name="baseLink">
4
+ <contact>
5
+ <lateral_friction value="1.0"/>
6
+ <inertia_scaling value="3.0"/>
7
+ </contact>
8
+ <inertial>
9
+ <origin rpy="0 0 0" xyz="-0.01 0 0.02"/>
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+ <mass value=".1"/>
11
+ <inertia ixx="1" ixy="0" ixz="0" iyy="1" iyz="0" izz="1"/>
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+ </inertial>
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+ <visual>
14
+ <origin rpy="0 0 0" xyz="0 0 0"/>
15
+ <geometry>
16
+ <mesh filename="textured-0008192.obj" scale="1.25 1.25 0.25"/>
17
+ </geometry>
18
+ <material name="green">
19
+ <color rgba="0.34901961 0.6627451 0.30980392 1"/>
20
+ </material>
21
+ </visual>
22
+ <collision>
23
+ <origin rpy="0 0 0" xyz="0 0 0"/>
24
+ <geometry>
25
+ <mesh filename="cup.obj" scale="1.25 1.25 0.25"/>
26
+ </geometry>
27
+ </collision>
28
+ </link>
29
+ </robot>