Learning a Distributed Hierarchical Locomotion Controller for Embodied Cooperation

2024/07/09 by Chuye Hong, Hong, Chuye, Kangyao Huang +3 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Human Motion and Animation #Multi-Agent Systems and Negotiation #Reinforcement Learning in Robotics #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2407.06499

openalex publication_date 2024/07/09 · openalex created_date 2024/07/11 · openalex updated_date 2026/07/28

Abstract

In this work, we propose a distributed hierarchical locomotion control strategy for whole-body cooperation and demonstrate the potential for migration into large numbers of agents. Our method utilizes a hierarchical structure to break down complex tasks into smaller, manageable sub-tasks. By incorporating spatiotemporal continuity features, we establish the sequential logic necessary for causal inference and cooperative behaviour in sequential tasks, thereby facilitating efficient and coordinated control strategies. Through training within this framework, we demonstrate enhanced adaptability and cooperation, leading to superior performance in task completion compared to the original methods. Moreover, we construct a set of environments as the benchmark for embodied cooperation.

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• Multi-agent Embodied AI: Advances and Future Directions

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