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.

翻译：在本研究中，我们提出了一种用于全身协作的分布式分层运动控制策略，并展示了其向大规模智能体迁移的潜力。该方法利用分层结构将复杂任务分解为更小、更易管理的子任务。通过引入时空连续性特征，我们建立了序列任务中因果推理与协作行为所需的时序逻辑，从而促进高效协调的控制策略。在此框架内进行训练后，我们证明了该方法具有更强的适应性与协作能力，在任务完成性能上优于原始方法。此外，我们构建了一套环境作为具身协作的基准测试平台。