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Summary

Multi-robot system research in IMCL can be divided into two parts:

  • Distributed Reinforcement Learning for Autonomous Cooperation: We aim to develop intelligent robots that can learn cooperative strategies from interactions with others. Specifically, we investigate the challenges of learning cooperation in a distributed way such as: partner modeling, partial observation problem and large-scale multi-robot system.
  • Middleware and Distributed Coordination of Multi-Robot Systems: Robotic middleware has become the key to reducing the complexity of multi-robot application development, existing works still have limitations in controlling multiple robots to perform missions cooperatively. We propose GraphWare, a novel middleware that provides a graph-oriented programming abstraction and its underlying runtime kernel for programming and building multi-robot cooperation applications.

Details

Distributed Reinforcement Learning for Autonomous Cooperation

Middleware and Distributed Coordination of Multi-Robot Systems