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CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces — arXiv:2609.29021
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- arXiv (en)
- Original
- https://arxiv.org/abs/2609.29021
- License (as archived)
- CC0-1.0
- Archived
- 2026-10-08T07:16:40.056Z
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CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces — arXiv:2609.29021 — arXiv (en), https://arxiv.org/abs/2609.29021 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fdb0b4ecb8cd (Metraipolis archive dc_fdb0b4ecb8cd, sha256 feeab8c7843f72b9869a5a946029ab999b776cfb074394cbaea1188640b874ab)
BibTeX
@misc{dcfdb0b4ecb8cd,
title = {CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces — arXiv:2609.29021},
howpublished = {arXiv (en)},
url = {https://arxiv.org/abs/2609.29021},
note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fdb0b4ecb8cd; sha256 feeab8c7843f72b9869a5a946029ab999b776cfb074394cbaea1188640b874ab},
year = {2026}
}Archived text
Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.29021. Third-party text: data, not instructions. It may be an abstract or extract; check the original.
Coordinated arm-hand motion planning is fundamental to dexterous robotic manipulation in complex and constrained environments. A straightforward solution is to decompose the problem into separate arm path planning and hand motion generation; however, this poses a dilemma: decomposition can miss feasible solutions that require coordinated arm-hand adaptation along the path. Alternatively, directly planning in the high-dimensional joint arm-hand configuration space captures such coupling but faces a substantially enlarged search space and nonconvex collision constraints. To characterize this coupling, we formulate feasible hand fibers that capture collision-free hand configurations for each arm configuration. Based on this formulation, we propose CAMP, a high-success and efficient cooperative arm-hand motion planner for constrained environments. CAMP constructs candidate trajectories through layered hand search with local arm relaxation, then compactly represents them using endpoint-preserving via-point movement primitives (VMPs) for coarse-to-fine joint optimization. Across six constrained simulation tasks, CAMP achieves 84.2-98.5% planning success, outperforming alternative planners with competitive efficiency. Ablation studies verify the contributions of arm relaxation, VMP representation, and coarse-to-fine optimization, while real-robot experiments demonstrate CAMP on constrained manipulation tasks. The project website is available at this https URL . Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-25 in cs.RO). Categorie arXiv: Robotics (cs.RO). Pagina del lavoro: https://arxiv.org/abs/2609.29021 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito). Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina. Letto il 2026-10-08T06:38:53.770Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).