发表机构
Harbin Institute of Technology, Shenzhen; Great Bay University; China Pharmaceutical University; KTH Royal Institute of Technology; The Hong Kong Polytechnic University; The University of Hong Kong; University of Belgrade（哈尔滨工业大学（深圳）; 大湾区大学; 中国药科大学; 瑞典皇家理工学院; 香港理工大学; 香港大学; 贝尔格莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对受限空间中机械臂-手部协同规划难题，提出CAMP规划器，通过手部纤维表征耦合、分层搜索与VMP优化，在六个任务中实现84.2%-98.5%成功率，验证了高效性与实用性。
AI 中文摘要
协调的机械臂-手部运动规划是复杂受限环境中灵巧机器人操作的基础。一种直接的解决方案是将问题分解为独立的机械臂路径规划和手部运动生成；然而，这带来了一个困境：分解可能会遗漏需要沿路径进行协同机械臂-手部适应的可行解决方案。或者，直接在高维的机械臂-手部关节联合配置空间中规划能够捕捉这种耦合，但面临搜索空间大幅扩大和非凸碰撞约束的问题。为了表征这种耦合，我们为每个机械臂配置构建了可行的“手部纤维”，以捕捉无碰撞的手部配置。基于这一公式，我们提出了CAMP，一种用于受限环境的高成功率和高效的协同机械臂-手部运动规划器。CAMP通过带有局部机械臂松弛的分层手部搜索构建候选轨迹，然后使用保持端点的路点运动原语（VMPs）对它们进行紧凑表示，以实现从粗到细的联合优化。在六个受限仿真任务中，CAMP实现了84.2%-98.5%的规划成功率，优于其他规划器且效率具有竞争力。消融研究验证了机械臂松弛、VMP表示和从粗到细优化的贡献，而真实机器人实验展示了CAMP在受限操作任务上的表现。项目网站可在该URL访问。
英文摘要
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 https://camp-armhand.github.io/.
