Learning a Distributed Hierarchical Locomotion Controller for Embodied Cooperation

TL;DR

This paper introduces a distributed hierarchical control strategy for multi-agent locomotion, enhancing cooperation and adaptability, and establishes a benchmark environment for embodied cooperation tasks.

Contribution

It presents a novel hierarchical control framework that improves multi-agent cooperation and adaptability, with a new benchmark environment for embodied cooperation.

Findings

Enhanced coordination and task performance in multi-agent systems

Effective handling of complex locomotion tasks through hierarchy

Benchmark environments for embodied cooperation

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.