{"ID":2870314,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12880","arxiv_id":"2509.12880","title":"Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation","abstract":"Pointing is a key mode of interaction with robots, yet most prior work has focused on recognition rather than generation. We present a motion capture dataset of human pointing gestures covering diverse styles, handedness, and spatial targets. Using reinforcement learning with motion imitation, we train policies that reproduce human-like pointing while maximizing precision. Results show our approach enables context-aware pointing behaviors in simulation, balancing task performance with natural dynamics.","short_abstract":"Pointing is a key mode of interaction with robots, yet most prior work has focused on recognition rather than generation. We present a motion capture dataset of human pointing gestures covering diverse styles, handedness, and spatial targets. Using reinforcement learning with motion imitation, we train policies that re...","url_abs":"https://arxiv.org/abs/2509.12880","url_pdf":"https://arxiv.org/pdf/2509.12880v1","authors":"[\"Anna Deichler\",\"Siyang Wang\",\"Simon Alexanderson\",\"Jonas Beskow\"]","published":"2025-09-16T09:30:42Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.HC\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}