Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control
<|reference_start|>Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control: Current humanoid push-recovery strategies often use whole-body motion, yet posture regulation is often overlooked. For instance, during manipulation tasks, the upper body may need to stay upright and have minimal recovery displacement. This paper introduces a novel approach to enhancing humanoid push-recovery performance under unknown disturbances and regulating body posture by tailoring the recovery stepping strategy. We propose a hierarchical-MPC-based scheme that analyzes and detects instability in the prediction window and quickly recovers through adapting gait frequency. Our approach integrates a high-level nonlinear MPC, a posture-aware gait frequency adaptation planner, and a low-level convex locomotion MPC. The planners predict the center of mass (CoM) state trajectories that can be assessed for precursors of potential instability and posture deviation. In simulation, we demonstrate improved maximum recoverable impulse by 131% on average compared with baseline approaches. In hardware experiments, a 125 ms advancement in recovery stepping timing/reflex has been observed with the proposed approach, We also demonstrate improved push-recovery performance and minimized attitude change under 0.2 rad.<|reference_end|>
@article{li2024adapting,
    title={Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery
  via Hierarchical Model Predictive Control},
    author={Junheng Li, Zhanhao Le, Junchao Ma, and Quan Nguyen},
    journal={arXiv preprint arXiv:2409.14342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14342},
    primaryClass={cs.RO cs.SY eess.SY}
}