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Two physical robots deployed with decentralized multi-agent reinforcement learning for joint task assignment and navigation

The authors deployed decentralized multi-agent reinforcement learning (MARL) on two physical robots for joint task assignment and navigation in a setting where multiple robots serve multiple production machines. Policies were first trained in high-fidelity simulation in Isaac Lab/Isaac Sim, then transferred zero-shot to the real robots via a self-built ROS 2 deployment pipeline. Paper: https://arxiv.org/abs/2609.14567.

Part of the event: Decentralized Multi-Agent Reinforcement Learning Deployed on Two Physical Robots

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