{"ID":2839452,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15239","arxiv_id":"2511.15239","title":"Symmetry-Breaking in Multi-Agent Navigation: Winding Number-Aware MPC with a Learned Topological Strategy","abstract":"In distributed multi-agent navigation without explicit communication, agents can fall into symmetry-induced deadlocks because each agent must autonomously decide how to pass others. To address this problem, we propose WNumMPC, a hierarchical navigation method that quantifies cooperative symmetry-breaking strategies via a topological invariant, the winding number, and learns such strategies through reinforcement learning. The learning-based Planner outputs continuous-valued signed target winding numbers and dynamic importance weights to prioritize critical interactions in dense crossings. Then, the model-based Controller generates collision-free and efficient motions based on the strategy and weights provided by the Planner. Simulation and real-world robot experiments indicate that WNumMPC effectively avoids deadlocks and collisions and achieves better performance than the baselines, particularly in dense and symmetry-prone scenarios. These experiments also suggest that explicitly leveraging winding numbers yields robust sim-to-real transfer with minimal performance degradation. The code for the experiments is available at https://github.com/omron-sinicx/WNumMPC.","short_abstract":"In distributed multi-agent navigation without explicit communication, agents can fall into symmetry-induced deadlocks because each agent must autonomously decide how to pass others. To address this problem, we propose WNumMPC, a hierarchical navigation method that quantifies cooperative symmetry-breaking strategies via...","url_abs":"https://arxiv.org/abs/2511.15239","url_pdf":"https://arxiv.org/pdf/2511.15239v2","authors":"[\"Tomoki Nakao\",\"Kazumi Kasaura\",\"Tadashi Kozuno\"]","published":"2025-11-19T08:47:03Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":606874,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2839452,"paper_url":"https://arxiv.org/abs/2511.15239","paper_title":"Symmetry-Breaking in Multi-Agent Navigation: Winding Number-Aware MPC with a Learned Topological Strategy","repo_url":"https://github.com/omron-sinicx/WNumMPC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}