{"ID":2891032,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18623","arxiv_id":"2507.18623","title":"Moving Out: Physically-grounded Human-AI Collaboration","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/.","short_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...","url_abs":"https://arxiv.org/abs/2507.18623","url_pdf":"https://arxiv.org/pdf/2507.18623v3","authors":"[\"Xuhui Kang\",\"Sung-Wook Lee\",\"Haolin Liu\",\"Yuyan Wang\",\"Yen-Ling Kuo\"]","published":"2025-07-24T17:57:18Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.MA\"]","methods":"[]","has_code":false}