1. An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
- Author
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Jermann, Tizian, Kolvenbach, Hendrik, Estay, Fidel Esquivel, Kramer, Koen, and Hutter, Marco
- Subjects
Computer Science - Robotics - Abstract
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.
- Published
- 2024