HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments

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 in environments with external disturbances. The external pulling 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. Furthermore, HAFO employs an asymmetric Actor-Critic framework in which the Critic network's access to privileged external forces guides the actor network to acquire generalizable force adaptation for resisting external disturbances. The experimental results demonstrate that HAFO achieves whole-body control for humanoid robots across diverse force-interaction environments, delivering outstanding performance in load-bearing tasks and maintaining stable operation even under rope suspension state.