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Trajectory guidance requires a leader robotic agent to assist a followerrobotic agent to cooperatively reach the target destination. However, planningcooperation becomes difficult when the leader serves a family of differentfollowers and has incomplete information about the followers. There is a needfor learning and fast adaptation of different cooperation plans. We develop aStackelberg meta-learning approach to address this challenge. We firstformulate the guided trajectory planning problem as a dynamic Stackelberg gameto capture the leader-follower interactions. Then, we leverage meta-learning todevelop cooperative strategies for different followers. The leader learns ameta-best-response model from a prescribed set of followers. When a specificfollower initiates a guidance query, the leader quickly adapts to thefollower-specific model with a small amount of learning data and uses it toperform trajectory guidance. We use simulations to elaborate that our methodprovides a better generalization and adaptation performance on learningfollowers' behavior than other learning approaches. The value and theeffectiveness of guidance are also demonstrated by the comparison with zeroguidance scenarios.

Stackelberg Meta-Learning for Strategic Guidance in Multi-Robot Trajectory Planning