{"ID":2855590,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12215","arxiv_id":"2510.12215","title":"Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications","abstract":"Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle avoidance and goal reaching. A sampling-based lookahead controller produces supervisory actions that are both safe and adaptive, which are subsequently distilled into a compact student policy suitable for real-time operation with uncertainty estimates. Experiments in synthetic and elevator co-boarding simulations show consistent gains in success rate and time efficiency over baselines, and real-world demonstrations with human participants confirm the practicality of deployment. A video illustrating this work can be found on our project page https://chanwookim971024.github.io/PioneeR/.","short_abstract":"Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and...","url_abs":"https://arxiv.org/abs/2510.12215","url_pdf":"https://arxiv.org/pdf/2510.12215v1","authors":"[\"Chanwoo Kim\",\"Jihwan Yoon\",\"Hyeonseong Kim\",\"Taemoon Jeong\",\"Changwoo Yoo\",\"Seungbeen Lee\",\"Soohwan Byeon\",\"Hoon Chung\",\"Matthew Pan\",\"Jean Oh\",\"Kyungjae Lee\",\"Sungjoon Choi\"]","published":"2025-10-14T07:10:15Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}