An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning

TL;DR

This paper presents a reinforcement learning-based strategy for multi-robot waste sorting that outperforms traditional methods, demonstrating improved efficiency in simulation and real hardware tests.

Contribution

It introduces a novel RL framework for multi-robot coordination in pick-and-place waste sorting tasks, with a custom OpenAI gym environment and superior performance over existing approaches.

Findings

Up to 16% higher picking rates in simulation.

Successful validation on a two-robot hardware setup.

Effective coordination strategy for waste sorting robots.

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.