HAFO让机器人在强干扰下仍能稳定行走并精准操控物体
HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments
- 双智能体强化学习协同优化行走与操作策略
- 单个策略即可应对负载、推力扰动等复杂环境
- 通过虚拟弹簧模型实现精细力控，自动抗干扰
强化学习控制器在人形机器人行走和轻量操作中取得显著进展，但在强交互力控制方面仍面临挑战。本文提出HAFO，一种双智能体强化学习框架，通过耦合训练同时优化鲁棒的行走策略与精确的上肢操作策略。采用约束残差动作空间提升双智能体训练的稳定性与采样效率。外部张力扰动通过弹簧阻尼系统显式建模，可通过对虚拟弹簧的调节实现细粒度力控，强化学习策略则自主利用环境反馈生成抗扰响应。实验结果表明，HAFO仅用单一双智能体策略即可实现人形机器人在多样化力交互环境下的全身控制，在负载承载与推力扰动条件下表现优异，且在绳索悬吊状态下仍保持稳定运行。
原文摘要 · Abstract (English)
Reinforcement learning (RL) controllers have made impressive progress in humanoid locomotion and light-weight object manipulation. However, achieving robust and precise motion control with intense force interaction remains a significant challenge. To address these limitations, this paper proposes HAFO, a dual-agent reinforcement learning framework that concurrently optimizes both a robust locomotion strategy and a precise upper-body manipulation strategy via coupled training. We employ a constrained residual action space to improve dual-agent training stability and sample efficiency. The external tension disturbances are explicitly modeled using a spring-damper system, allowing for fine-grained force control through manipulation of the virtual spring. In this process, the reinforcement learning policy autonomously generates a disturbance-rejection response by utilizing environmental feedback. The experimental results demonstrate that HAFO achieves whole-body control for humanoid robots across diverse force-interaction environments using a single dual-agent policy, delivering outstanding performance under load-bearing and thrust-disturbance conditions, while maintaining stable operation even under rope suspension state.
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