LLM_4_robotics

OVERVIEW

LLM-4-Robotics is an organization dedicated to exploring the integration of Foundation Models (FMs) in robotics applications. Our aim is to leverage the power of advanced natural language processing techniques to enhance various aspects of robotics, including perception, reasoning, planning, and navigation.

PURPOSE

The purpose of LLM-4-Robotics is to:

• Investigate and develop innovative approaches for incorporating LLMs into robotics systems.
• Explore the potential of LLMs in enhancing robot perception, understanding of human language and intent, and decision-making capabilities.
• Bridge the gap between natural language understanding and robotic actions, enabling more intuitive human-robot interaction.
• Contribute to the advancement of robotics research by leveraging state-of-the-art language models.

LITERATURE

Survey Papers

• "Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis", arXiv, Dec 2023. [Paper] [Paper List] [Website]
• "Language-conditioned Learning for Robotic Manipulation: A Survey", arXiv, Dec 2023, [Paper]
• "Foundation Models in Robotics: Applications, Challenges, and the Future", arXiv, Dec 2023, [Paper] [Paper List]
• "Robot Learning in the Era of Foundation Models: A Survey", arXiv, Nov 2023, [Paper]
• "The Development of LLMs for Embodied Navigation", arXiv, Nov 2023, [Paper]

Planning

• BTGenBot: "BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs", arXiv, March 2024. [Paper][Github]
• Attentive Support: "To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions", arXiv, March 2024. [Paper] [Website][Code]
• SayCanPay: "SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge", AAAI Jan 2024, [Paper] [Code] [Website]
• ViLa: "Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning", arXiv, Sep 2023, [Paper] [Website]
• -: "When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications", arXiv, Oct 2023. [Paper]
• CoPAL: "Corrective Planning of Robot Actions with Large Language Models", ICRA, Oct 2023. [Paper] [Website][Code]
• LGMCTS: "LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement", arXiv, Sep 2023. [Paper]
• Prompt2Walk: "Prompt a Robot to Walk with Large Language Models", arXiv, Sep 2023, [Paper] [Website]
• DoReMi: "Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment", arXiv, July 2023, [Paper] [Website]
• Co-LLM-Agents: "Building Cooperative Embodied Agents Modularly with Large Language Models", arXiv, Jul 2023. [Paper] [Code] [Website]
• LLM-Reward: "Language to Rewards for Robotic Skill Synthesis", arXiv, Jun 2023. [Paper] [Website]
• LLM-BRAIn: "LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model", arXiv, May 2023. [Paper]
• GLAM: "Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning", arXiv, May 2023. [Paper] [Pytorch Code]
• LLM-MCTS: "Large Language Models as Commonsense Knowledge for Large-Scale Task Planning", arXiv, May 2023. [Paper]
• AlphaBlock: "AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation", arxiv, May 2023. [Paper]
• LLM+P:"LLM+P: Empowering Large Language Models with Optimal Planning Proficiency", arXiv, Apr 2023, [Paper] [Code]
• ReAct: "ReAct: Synergizing Reasoning and Acting in Language Models", ICLR, Apr 2023. [Paper] [Github] [Website]
• LLM-planner: "LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models", arXiv, Mar 2023. [Paper] [Pytorch Code] [Website]
• Text2Motion: "Text2Motion: From Natural Language Instructions to Feasible Plans", arXiV, Mar 2023, [Paper] [Website]
• "Reward Design with Language Models", ICML, Feb 2023. [Paper] [Pytorch Code]
• Don't Copy the Teacher: "Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue", EMNLP, Oct 2022. [Paper] [Website]
• FILM: "FILM: Following Instructions in Language with Modular Methods", ICLR, Apr 2022. [Paper] [Code] [Website]
• LID: "Pre-Trained Language Models for Interactive Decision-Making", arXiv, Feb 2022. [Paper] [Pytorch Code] [Website]
• "Collaborating with language models for embodied reasoning", NeurIPS, Feb 2022. [Paper]
• ZSP: "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents", ICML, Jan 2022. [Paper] [Pytorch Code] [Website]
• "Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions", arXiV, Oct 2020, [Paper]

Reasoning

• CLEAR: "Language, Camera, Autonomy! Prompt-engineered Robot Control for Rapidly Evolving Deployment", ACM/IEEE International Conference on Human-Robot Interaction (HRI), Mar 2024. [Paper] [Code]
• MoMa-LLM: "Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation", arXiv, Mar 2024. [Paper] [Code] [Website]
• AutoRT: "Embodied Foundation Models for Large Scale Orchestration of Robotic Agents", arXiv, Jan 2024. [Paper] [Website]
• LEO: "An Embodied Generalist Agent in 3D World", arXiv, Nov 2023. [Paper] [Code] [Website]
• Robogen: "A generative and self-guided robotic agent that endlessly propose and master new skills.", arXiv, Nov 2023. [Paper] [Code] [Website]
• SayPlan: "Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning", Conference on Robot Learning (CoRL), Nov 2023. [Paper] [Website]
• [LLaRP] "Large Language Models as Generalizable Policies for Embodied Tasks", arXiv, Oct 2023. [Paper] [Website]
• [RT-X] "Open X-Embodiment: Robotic Learning Datasets and RT-X Models", arXiv, July 2023. [Paper] [Website]
• [RT-2] "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control", arXiv, July 2023. [Paper] [Website]
• Instruct2Act: "Mapping Multi-modality Instructions to Robotic Actions with Large Language Model", arXiv, May 2023. [Paper] [Pytorch Code]
• TidyBot: "Personalized Robot Assistance with Large Language Models", arXiv, May 2023. [Paper] [Pytorch Code] [Website]
• Generative Agents: "Generative Agents: Interactive Simulacra of Human Behavior", arXiv, Apr 2023. [Paper Code]
• Matcha: "Chat with the Environment: Interactive Multimodal Perception using Large Language Models", IROS, Mar 2023. [Paper] [Github] [Website]
• PaLM-E: "PaLM-E: An Embodied Multimodal Language Model", arXiv, Mar 2023, [Paper] [Webpage]
• "Large Language Models as Zero-Shot Human Models for Human-Robot Interaction", arXiv, Mar 2023. [Paper]
• CortexBench "Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?" arXiv, Mar 2023. [Paper]
• "Translating Natural Language to Planning Goals with Large-Language Models", arXiv, Feb 2023. [Paper]
• RT-1: "RT-1: Robotics Transformer for Real-World Control at Scale", arXiv, Dec 2022. [Paper] [GitHub] [Website]
• "PDDL Planning with Pretrained Large Language Models", NeurIPS, Oct 2022. [Paper] [Github]
• ProgPrompt: "Generating Situated Robot Task Plans using Large Language Models", arXiv, Sept 2022. [Paper] [Github] [Website]
• Code-As-Policies: "Code as Policies: Language Model Programs for Embodied Control", arXiv, Sept 2022. [Paper] [Colab] [Website]
• PIGLeT: "PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World", ACL, Jun 2021. [Paper] [Pytorch Code] [Website]
• Say-Can: "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances", arXiv, Apr 2021. [Paper] [Colab] [Website]
• Socratic: "Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language", arXiv, Apr 2021. [Paper] [Pytorch Code] [Website]

Navigation

• Navid: "NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation", arxiv, Mar 2024 [Paper] [Website]
• OVSG: "Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs", CoRL, Nov 2023. [Paper] [Code] [Website]
• VLMaps: "Visual Language Maps for Robot Navigation", arXiv, Mar 2023. [Paper] [Pytorch Code] [Website]
• "Interactive Language: Talking to Robots in Real Time", arXiv, Oct 2022 [Paper] [Website]
• NLMap:"Open-vocabulary Queryable Scene Representations for Real World Planning", arXiv, Sep 2022, [Paper] [Website]
• ADAPT: "ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts", CVPR, May 2022. [Paper]
• "The Unsurprising Effectiveness of Pre-Trained Vision Models for Control", ICML, Mar 2022. [Paper] [Pytorch Code] [Website]
• CoW: "CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration", arXiv, Mar 2022. [Paper]
• Recurrent VLN-BERT: "A Recurrent Vision-and-Language BERT for Navigation", CVPR, Jun 2021 [Paper] [Pytorch Code]
• VLN-BERT: "Improving Vision-and-Language Navigation with Image-Text Pairs from the Web", ECCV, Apr 2020 [Paper] [Pytorch Code]