An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
<|reference_start|>An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning: We introduce a novel strategy for multi-robot sorting of waste objects using Reinforcement Learning. Our focus lies on finding optimal picking strategies that facilitate an effective coordination of a multi-robot system, subject to maximizing the waste removal potential. We realize this by formulating the sorting problem as an OpenAI gym environment and training a neural network with a deep reinforcement learning algorithm. The objective function is set up to optimize the picking rate of the robotic system. In simulation, we draw a performance comparison to an intuitive combinatorial game theory-based approach. We show that the trained policies outperform the latter and achieve up to 16% higher picking rates. Finally, the respective algorithms are validated on a hardware setup consisting of a two-robot sorting station able to process incoming waste objects through pick-and-place operations.<|reference_end|>
@article{jermann2024an,
    title={An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place
  Tasks using Reinforcement Learning},
    author={Tizian Jermann, Hendrik Kolvenbach, Fidel Esquivel Estay, Koen Kramer,
  Marco Hutter},
    journal={arXiv preprint arXiv:2409.13511},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13511},
    primaryClass={cs.RO}
}