Distributed Differentiable Dynamic Game for Multi-robot Coordination

TL;DR

This paper introduces a Distributed Differentiable Dynamic Game framework that enables multi-robot systems to collaboratively find equilibrium strategies and learn objectives in a distributed manner, improving coordination efficiency.

Contribution

The novel D3G framework formulates multi-robot coordination as a dynamic game and provides distributed algorithms for both forward equilibrium seeking and inverse learning tasks.

Findings

D3G effectively finds Nash equilibria in multi-robot systems.

D3G accurately learns robot objectives from demonstrations.

Simulation results outperform existing methods.

Abstract

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.