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.
@article{arxiv.2410.00757,
title = {Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives},
author = {Siddharth Singh and Tian Xu and Qing Chang},
journal= {arXiv preprint arXiv:2410.00757},
year = {2025}
}