Papers
arxiv:2409.20551

UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models

Published on Sep 30
· Submitted by SiyuanH on Oct 1
Authors:
,
,
,
,
,
,
,
,

Abstract

Previous studies on robotic manipulation are based on a limited understanding of the underlying 3D motion constraints and affordances. To address these challenges, we propose a comprehensive paradigm, termed UniAff, that integrates 3D object-centric manipulation and task understanding in a unified formulation. Specifically, we constructed a dataset labeled with manipulation-related key attributes, comprising 900 articulated objects from 19 categories and 600 tools from 12 categories. Furthermore, we leverage MLLMs to infer object-centric representations for manipulation tasks, including affordance recognition and reasoning about 3D motion constraints. Comprehensive experiments in both simulation and real-world settings indicate that UniAff significantly improves the generalization of robotic manipulation for tools and articulated objects. We hope that UniAff will serve as a general baseline for unified robotic manipulation tasks in the future. Images, videos, dataset, and code are published on the project website at:https://sites.google.com/view/uni-aff/home

Community

Paper author Paper submitter
·

Hi @SiyuanH congrats on this work!

I opened https://huggingface.co/SiyuanH/UniAff-13B/discussions/1 to link it to the paper page, and add a model card. Would be great to do the same once the dataset is out :) see here for a guide: https://huggingface.co/docs/datasets/loading.

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

The following papers were recommended by the Semantic Scholar API

Please give a thumbs up to this comment if you found it helpful!

If you want recommendations for any Paper on Hugging Face checkout this Space

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend

Sign up or log in to comment

Models citing this paper 1

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2409.20551 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2409.20551 in a Space README.md to link it from this page.

Collections including this paper 2