Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration

TL;DR

This paper introduces a game-theoretic framework using Stackelberg games to coordinate heterogeneous robots with one-way communication, enhancing efficiency and robustness in multi-object rearrangement tasks.

Contribution

It develops a novel theoretical and algorithmic approach for strategic guidance in heterogeneous robot collaboration using Stackelberg equilibria.

Findings

Improved efficiency in robot rearrangement tasks.

Robustness to model uncertainty and collaboration pitfalls.

Effective coordination with one-way communication.

Abstract

In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robots with one-way communication. To that end, focusing on the task of multi-object rearrangement, we develop a theoretical and algorithmic framework that provides strategic guidance for a pair of robot arms, a leader and a follower where the leader has a model of the follower's decision-making process, through the computation of a feedback Stackelberg equilibrium. With built-in tolerance of model uncertainty, the strategic guidance generated by our planning algorithm not only improves the overall efficiency in solving the rearrangement tasks, but is also robust to common pitfalls in collaboration, e.g., chattering.