论文标题
狐猴：学习分布式多机器人相互作用
LEMURS: Learning Distributed Multi-Robot Interactions
论文作者
论文摘要
本文介绍了狐猴，这是一种从合作任务演示中学习可扩展的多机器人控制政策的算法。我们建议对多机器人系统的港口 - 哈米尔顿港描述，以利用互连系统中的通用物理约束并实现闭环稳定性。我们使用结合自我注意机制和神经普通微分方程的体系结构代表多机器人控制策略。前者在机器人团队中处理时变的沟通，而后者则尊重连续的机器人动力学。我们的表示是通过施工来分配的，使学习的控制政策能够部署在不同尺寸的机器人团队中。我们证明，狐猴可以通过演示多代理导航和羊群任务来学习互动和合作行为。
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.
