Hierarchical Control for Robust Standing Stability and Fall Recovery of Task-Performing Humanoid Robots

Abstract

Maintaining robust standing stability is critical for humanoid robots performing precision manipulation tasks, where traditional balance strategies (e.g., stepping) can disrupt task execution. Designing a controller that ensures both stability and effective disturbance rejection is challenging, as these goals impose conflicting requirements on body sway and stepping behavior. To address this, we propose a hierarchical control framework that decouples these objectives into three parts: (1) a Task-Oriented Standing Policy trained via reinforcement learning with strict penalties on stepping and body sway, specialized for non-stepping disturbance rejection; (2) Ankle Strategy Modulation embedded within the standing policy, which dynamically adjusts the Zero-Moment Point (ZMP) to eliminate Divergent Component of Motion (DCM) errors, thereby accelerating recovery from disturbances; and (3) a fall prediction and recovery mechanism that triggers a transition to a robust stepping policy when disturbances exceed the stability threshold. Extensive experiments on a Q-series humanoid robot, in both simulation and hardware, validate the controller’s effectiveness. We achieved a 100% task success rate over 100 trials in practical scenarios including robot archery and part grasping/placement.