Stackelberg Game-Theoretic Learning for Collaborative Assembly Task Planning
Yuhan Zhao, Lan Shi, Quanyan Zhu
TL;DR
The paper tackles scalable coordination for two heterogeneous robots in collaborative assembly, where centralized planning struggles with increasing task diversity. It formulates a stochastic Stackelberg game to capture leader–follower interactions and introduces Stackelberg double deep Q-learning to learn equilibrium strategies for both robots. Through simulations on eight assembly tasks, the method outperforms independent Q-learning, Nash Q-learning, and MADDPG, achieving faster completion times and robustness to disturbances. The approach leverages a chessboard representation to decompose tasks and reduce learning complexity, enabling automated, robust, and efficient multi-robot collaboration for customized assembly scenarios.
Abstract
As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordinating diverse robots for collaborative assembly. To this end, we propose a Stackelberg game-theoretic learning approach. By leveraging Stackelberg games, we characterize robot collaboration through leader-follower interaction to enhance strategy seeking and ensure task completion. To enhance applicability across tasks, we introduce a novel multi-agent learning algorithm: Stackelberg double deep Q-learning, which facilitates automated assembly strategy seeking and multi-robot coordination. Our approach is validated through simulated assembly tasks. Comparison with three alternative multi-agent learning methods shows that our approach achieves the shortest task completion time for tasks. Furthermore, our approach exhibits robustness against both accidental and deliberate environmental perturbations.