Coordination of Learned Decoupled Dual-Arm Tasks through Gaussian Belief Propagation

Abstract

Robotic manipulation can involves multiple manipulators to complete a task. In those cases, the complexity of performing the task in a coordinated manner increases, requiring coordinated planning while avoiding collisions between robots and environmental elements. For these challenges, we propose a robotic arm control algorithm based on Learning from Demonstration to independently learn the tasks of each arm, followed by a graph-based communication method using Gaussian Belief Propagation. Our method enables the resolution of decoupled dual-arm tasks learned independently with-out requiring coordinated planning. The algorithm generates smooth, collision-free solutions between arms and environmental obstacles while ensuring efficient movements without the need for constant replanning. Its efficiency has been validated through experiments and comparisons against another multi-robot control method in simulation using PyBullet with two opposing IIWA robots, as well as a mobile robot with two UR3 arms, which has also been used for real-world testing. Code provided in https://adrianprados.github.io/GaussianBeliefPropagationDualArm/