  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.
