{"ID":2830992,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08206","arxiv_id":"2512.08206","title":"High-Performance Dual-Arm Task and Motion Planning for Tabletop Rearrangement","abstract":"We propose Synchronous Dual-Arm Rearrangement Planner (SDAR), a task and motion planning (TAMP) framework for tabletop rearrangement, where two robot arms equipped with 2-finger grippers must work together in close proximity to rearrange objects whose start and goal configurations are strongly entangled. To tackle such challenges, SDAR tightly knit together its dependency-driven task planner (SDAR-T) and synchronous dual-arm motion planner (SDAR-M), to intelligently sift through a large number of possible task and motion plans. Specifically, SDAR-T applies a simple yet effective strategy to decompose the global object dependency graph induced by the rearrangement task, to produce more optimal dual-arm task plans than solutions derived from optimal task plans for a single arm. Leveraging state-of-the-art GPU SIMD-based motion planning tools, SDAR-M employs a layered motion planning strategy to sift through many task plans for the best synchronous dual-arm motion plan while ensuring high levels of success rate. Comprehensive evaluation demonstrates that SDAR delivers a 100% success rate in solving complex, non-monotone, long-horizon tabletop rearrangement tasks with solution quality far exceeding the previous state-of-the-art. Experiments on two UR-5e arms further confirm SDAR directly and reliably transfers to robot hardware. Source code and supplementary materials are available at https://github.com/arc-l/dual-arm.","short_abstract":"We propose Synchronous Dual-Arm Rearrangement Planner (SDAR), a task and motion planning (TAMP) framework for tabletop rearrangement, where two robot arms equipped with 2-finger grippers must work together in close proximity to rearrange objects whose start and goal configurations are strongly entangled. To tackle such...","url_abs":"https://arxiv.org/abs/2512.08206","url_pdf":"https://arxiv.org/pdf/2512.08206v2","authors":"[\"Duo Zhang\",\"Junshan Huang\",\"Jingjin Yu\"]","published":"2025-12-09T03:33:34Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":606089,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2830992,"paper_url":"https://arxiv.org/abs/2512.08206","paper_title":"High-Performance Dual-Arm Task and Motion Planning for Tabletop Rearrangement","repo_url":"https://github.com/arc-l/dual-arm","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}