When: Wednesday, April 27th at 15:00
Abstract: In this talk, I first discuss how we leverage machine learning methods to generate cooperative policies for multi-robot systems. I describe how we use Graph Neural Networks (GNNs) to learn effective communication strategies for decentralized coordination. I then show how our GNN-based policy is able to achieve near-optimal performance across a variety of problems, at a fraction of the real-time computational cost. Finally, I present some pioneering real-robot experiments that demonstrate the transfer of our methods to the physical world.