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

• URL: http://arxiv.org/abs/2504.15876v2
• Date: Wed, 23 Apr 2025 15:00:10 GMT
• Title: Bidirectional Task-Motion Planning Based on Hierarchical Reinforcement Learning for Strategic Confrontation
• Authors: Qizhen Wu, Lei Chen, Kexin Liu, Jinhu Lü,
• Abstract summary: In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision-making.<n>Traditional task and motion planning methods separate decision-making into two layers, but their unidirectional structure fails to capture the interdependence between these layers.<n>Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers.
• Score: 12.122881147337505
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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