{"ID":2858457,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08811","arxiv_id":"2510.08811","title":"Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration","abstract":"Human-robot collaboration (HRC) requires robots to adapt their motions to human intent to ensure safe and efficient cooperation in shared spaces. Although large language models (LLMs) provide high-level reasoning for inferring human intent, their application to reliable motion planning in HRC remains challenging. Physical human-robot interaction (pHRI) is intuitive but often relies on continuous kinesthetic guidance, which imposes burdens on operators. To address these challenges, a contact-informed adaptive motion-planning framework is introduced to infer human intent directly from physical contact and employ the inferred intent for online motion correction in HRC. First, an optimization-based force estimation method is proposed to infer human-intended contact forces and locations from joint torque measurements and a robot dynamics model, thereby reducing cost and installation complexity while enabling whole-body sensitivity. Then, a torque-based contact detection mechanism with link-level localization is introduced to reduce the optimization search space and to enable real-time estimation. Subsequently, a contact-informed adaptive motion planner is developed to infer human intent from contacts and to replan robot motion online, while maintaining smoothness and adapting to human corrections. Finally, experiments on a 7-DOF manipulator are conducted to demonstrate the accuracy of the proposed force estimation method and the effectiveness of the contact-informed adaptive motion planner under perception uncertainty in HRC.","short_abstract":"Human-robot collaboration (HRC) requires robots to adapt their motions to human intent to ensure safe and efficient cooperation in shared spaces. Although large language models (LLMs) provide high-level reasoning for inferring human intent, their application to reliable motion planning in HRC remains challenging. Physi...","url_abs":"https://arxiv.org/abs/2510.08811","url_pdf":"https://arxiv.org/pdf/2510.08811v1","authors":"[\"Jiurun Song\",\"Xiao Liang\",\"Minghui Zheng\"]","published":"2025-10-09T20:58:09Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}