Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives

TL;DR

This paper introduces a multi-level method combining Reinforcement Learning and Dynamic Movement Primitives to enable collision-free, adaptive, real-time collaborative motion planning for multi-manipulator robotic systems in dynamic environments.

Contribution

It presents a novel integration of RL and DMPs for multi-robot motion planning, addressing collision avoidance and adaptability in complex tasks.

Findings

Successful collision-free trajectory generation in simulation

Effective collaboration among multiple manipulators

Real-time adaptation to dynamic environments

Abstract

Robotic tasks often require multiple manipulators to enhance task efficiency and speed, but this increases complexity in terms of collaboration, collision avoidance, and the expanded state-action space. To address these challenges, we propose a multi-level approach combining Reinforcement Learning (RL) and Dynamic Movement Primitives (DMP) to generate adaptive, real-time trajectories for new tasks in dynamic environments using a demonstration library. This method ensures collision-free trajectory generation and efficient collaborative motion planning. We validate the approach through experiments in the PyBullet simulation environment with UR5e robotic manipulators.