Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives
<|reference_start|>Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives: 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.<|reference_end|>
@article{singh2024collaborative,
    title={Collaborative motion planning for multi-manipulator systems through
  Reinforcement Learning and Dynamic Movement Primitives},
    author={Siddharth Singh, Tian Xu and Qing Chang},
    journal={arXiv preprint arXiv:2410.00757},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00757},
    primaryClass={cs.RO}
}