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papersTODAY 04:00 UTC

Multi-Agent RL Approach to Factory Task Assignment and Navigation Tested on Real Robots

A new arXiv paper examines how multi-agent reinforcement learning can be applied to task assignment and navigation for robot fleets in industrial settings. The authors focus on the gap between simulation training and deployment on physical multi-robot systems, a step that remains difficult in practice. The work reports on transferring learned policies from simulated environments to real factory robots.

arXivindustrial-roboticsmulti-agent-reinforcement-learningrobot-navigationsim-to-real transfer

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGLearning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots ↗TODAY 04:00 UTC

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