Cooperative arm hand planning improves robot motion in tight spaces
CAMP: Cooperative Arm-Hand Motion Planning in Constrained Spaces
Robotics
Summary
Robots with arms and hands need to move both parts together to handle things well in small or tricky spaces. The authors found that separating planning for arm movement and hand movement can miss solutions where the arm and hand need to adjust together. They developed CAMP, a method that plans arm and hand motions together efficiently by breaking down the problem and improving search steps. Their approach worked well in simulations and real robot tests, successfully planning motions more often than other methods.
What this means in practice
- •For robotic system developers: Plan coordinated arm and hand movements for robots working in confined, cluttered environments to perform dexterous tasks effectively.
- •For industrial automation engineers: Enable robots to better manipulate objects in tight factory spaces by integrating arm-hand motion planning into control software.
Authors
Ziyuan Wang, Yunlong Shan, Fei Mo, Sichao Liu, David Navarro-Alarcon, Jia Pan, Kosta Jovanovic, Xin Jiang, Peng Zhou
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/.