论文标题
分布式可微分的动态游戏用于多机器人协调
Distributed Differentiable Dynamic Game for Multi-robot Coordination
论文作者
论文摘要
本文开发了一个分布式可区分的动态游戏（D3G）框架，该框架可以有效地解决多机器人协调中的前进和反向问题。我们将多机器人协调制定为动态游戏，其中机器人的行为由其自身的动态和目标决定，这也取决于他人的行为。在远期问题中，D3G通过开发基于分布式射击的NASH求解器，使所有机器人都可以协作以分布式方式寻求游戏的NASH均衡。在逆问题中，每个机器人旨在查找（学习）其目标（和动态）参数以模拟给定协调性演示，而D3G提出了基于差分庞特拉金的最大原理的分化求解器，这使每个机器人都可以在分布式和协调的方式中更新其参数。我们在模拟中使用两种类型的机器人测试D3G，给定不同的任务配置。结果证明了与现有方法相比，D3G在解决前进和反问题方面的有效性。
This paper develops a Distributed Differentiable Dynamic Game (D3G) framework, which can efficiently solve the forward and inverse problems in multi-robot coordination. We formulate multi-robot coordination as a dynamic game, where the behavior of a robot is dictated by its own dynamics and objective that also depends on others' behavior. In the forward problem, D3G enables all robots collaboratively to seek the Nash equilibrium of the game in a distributed manner, by developing a distributed shooting-based Nash solver. In the inverse problem, where each robot aims to find (learn) its objective (and dynamics) parameters to mimic given coordination demonstrations, D3G proposes a differentiation solver based on Differential Pontryagin's Maximum Principle, which allows each robot to update its parameters in a distributed and coordinated manner. We test the D3G in simulation with two types of robots given different task configurations. The results demonstrate the effectiveness of D3G for solving both forward and inverse problems in comparison with existing methods.
