Moving Out: Physically-grounded Human-AI Collaboration

• URL: http://arxiv.org/abs/2507.18623v2
• Date: Sat, 26 Jul 2025 03:07:12 GMT
• Title: Moving Out: Physically-grounded Human-AI Collaboration
• Authors: Xuhui Kang, Sung-Wook Lee, Haolin Liu, Yuyan Wang, Yen-Ling Kuo,
• Abstract summary: We introduce Moving Out, a new human-AI collaboration benchmark.<n>We evaluate models' abilities to adapt to diverse human behaviors and unseen physical attributes.<n>Our experiments show that BASS outperforms state-of-the-art models in AI-AI and human-AI collaboration.
• Score: 10.515976351631666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to adapt to physical actions and constraints in an environment is crucial for embodied agents (e.g., robots) to effectively collaborate with humans. Such physically grounded human-AI collaboration must account for the increased complexity of the continuous state-action space and constrained dynamics caused by physical constraints. In this paper, we introduce Moving Out, a new human-AI collaboration benchmark that resembles a wide range of collaboration modes affected by physical attributes and constraints, such as moving heavy items together and maintaining consistent actions to move a big item around a corner. Using Moving Out, we designed two tasks and collected human-human interaction data to evaluate models' abilities to adapt to diverse human behaviors and unseen physical attributes. To address the challenges in physical environments, we propose a novel method, BASS (Behavior Augmentation, Simulation, and Selection), to enhance the diversity of agents and their understanding of the outcome of actions. Our experiments show that BASS outperforms state-of-the-art models in AI-AI and human-AI collaboration. The project page is available at https://live-robotics-uva.github.io/movingout_ai/.

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