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LEMURS: Learning Distributed Multi-Robot Interactions
This paper presents LEMURS, an algorithm for learning scalable multi-robot
control policies from cooperative task demonstrations. We propose a
port-Hamiltonian description of the multi-robot system to exploit universal
physical constraints in interconnected systems and achieve closed-loop
stability. We represent a multi-robot control policy using an architecture that
combines self-attention mechanisms and neural ordinary differential equations.
The former handles time-varying communication in the robot team, while the
latter respects the continuous-time robot dynamics. Our representation is
distributed by construction, enabling the learned control policies to be
deployed in robot teams of different sizes. We demonstrate that LEMURS can
learn interactions and cooperative behaviors from demonstrations of multi-agent
navigation and flocking tasks.Comment: In Submissio
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