{"ID":2830724,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-06T23:32:08.005116109Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09656","arxiv_id":"2512.09656","title":"ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat","abstract":"Reactive control can gracefully coordinate the motion of the base and the arm of a mobile manipulator. However, incorporating an accurate representation of the environment to avoid obstacles without involving costly planning remains a challenge. In this work, we present ReMoSPLAT, a reactive controller based on a quadratic program formulation for mobile manipulation that leverages a Gaussian Splat representation for collision avoidance. By integrating additional constraints and costs into the optimisation formulation, a mobile manipulator platform can reach its intended end effector pose while avoiding obstacles, even in cluttered scenes. We investigate the trade-offs of two methods for efficiently calculating robot-obstacle distances, comparing a purely geometric approach with a rasterisation-based approach. Our experiments in simulation on both synthetic and real-world scans demonstrate the feasibility of our method, showing that the proposed approach achieves performance comparable to controllers that rely on perfect ground-truth information.","short_abstract":"Reactive control can gracefully coordinate the motion of the base and the arm of a mobile manipulator. However, incorporating an accurate representation of the environment to avoid obstacles without involving costly planning remains a challenge. In this work, we present ReMoSPLAT, a reactive controller based on a quadr...","url_abs":"https://arxiv.org/abs/2512.09656v1","url_pdf":"https://arxiv.org/pdf/2512.09656v1","authors":"Nicolas Marticorena, Tobias Fischer, Niko Suenderhauf","published":"2025-12-10T13:52:08Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}