Bidirectional Task–Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation

Qizhen Wu, Lei Chen, Jinhu Lu

Abstract: In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision– making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision–making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting adaptability in dynamic environments. Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers. This method effectively maps commands to task allocation and actions to path planning, while leveraging cross– training techniques to enhance learning across the hierarchical framework. Furthermore, we introduce a trajectory prediction model that bridges abstract task representations with actionable planning goals. In our experiments, it achieves over 80% in confrontation win rate and under 0.01 seconds in decision time, outperforming existing approaches. Demonstrations through large–scale tests and real–world robot experiments further emphasize the generalization capabilities and practical applicability of our method.