Physical Simulation for Multi-agent Multi-machine Tending

TL;DR

This paper demonstrates how reinforcement learning can be effectively applied to small-scale robotic systems in a real-world setting, providing insights into deployment challenges and behavior replication from simulation.

Contribution

It introduces a practical approach to using RL with simple robots in a real environment, bridging the gap between simulation and real-world deployment.

Findings

Robots mimicked simulated behavior despite size and dynamics differences

Real-world experiments revealed deployment challenges

RL enabled effective multi-agent coordination in physical robots

Abstract

The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment. In this work, we leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting. A real-world tabletop arena was designed with robots that mimic the agents' behavior in the simulation. Despite the difference in dynamics and machine size, the robots were able to depict the same behavior as in the simulation. In addition, those experiments provided an initial understanding of the real deployment challenges.