用强化学习优化多机械臂分拣效率，提升16%抓取率。
An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
- 将分拣任务建模为OpenAI Gym环境，用深度强化学习训练策略。
- 仿真中比博弈论方法提升16%抓取率，硬件验证可行。
- 适合做智能物流、垃圾分类等多机器人协同场景的研究者。
我们提出一种基于强化学习的多机器人废弃物分拣新策略，旨在寻找最优抓取方案以实现多机器人系统的高效协同，最大化废物清除能力。通过将分拣问题建模为OpenAI Gym环境，并利用深度强化学习算法训练神经网络，优化机器人系统的抓取率。在仿真中，与基于组合博弈论的直观方法进行性能对比，结果表明所训练策略表现更优，抓取率最高提升16%。最后，相关算法在包含两台机器人组成的分拣工作站的硬件平台上得到验证，能够完成连续的拾取-放置操作，处理流入的废弃物。
原文摘要 · Abstract (English)
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.
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