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

qiaojun yu , Siyuan, Xibin Yuan, zkjiang, Ce Hao, Xin Li, Haonan Chang, Junbo Wang, Liu Liu, Hongsheng Li, Peng Gao, Cewu Lu

UniAff: A Unified Representation of Affordances for Tool Usage and Articulation with Vision-Language Models: 14 upvotes on Hugging Face Daily Papers, #7 of 13 papers on 2024-10-01. Day-by-day upvote history.

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

Paper page on Hugging Face · arXiv

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