CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces

2026-09-24

AI Summary

This study addresses the limitations of decomposition-based methods, which often miss feasible solutions, and the inefficiency of high-dimensional search in coordinated planning for robotic arms and dexterous hands within confined spaces. To this end, we propose CAMP, a coordinated planning framework whose core innovation lies in constructing feasible hand fibers to precisely characterize arm-hand coupling. The method combines hierarchical hand search with local arm relaxation to generate initial trajectories, and introduces endpoint-preserving Via-point Movement Primitives (VMPs) to enable compact representation and coarse-to-fine joint optimization. Experimental results demonstrate that CAMP achieves success rates ranging from 84.2% to 98.5% across six simulated tasks, significantly outperforming existing baselines. Furthermore, real-world experiments validate its practical effectiveness on physical hardware.

Abstract

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/.

Problem

• arm-hand motion planning
• dexterous manipulation
• constrained environments
• high-dimensional configuration space
• collision constraints

Innovation

• Cooperative motion planning
• Arm-hand coordination
• Feasible hand fibers
• Via-point movement primitives
• Constrained manipulation