通过调整步频实现姿态调节，提升人形机器人抗推力恢复能力。
Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control
- 分层模型预测控制，动态调节步频以应对扰动
- 仿真中最大可恢复冲击力提升131%，硬件响应提前125毫秒
- 适合需要稳定上身姿态的复杂操作任务
当前人形机器人抗推力恢复策略多采用全身运动，却常忽略姿态调节。例如在操作任务中，上半身需保持直立且恢复位移最小。本文提出一种新方法，通过定制恢复步态策略，在未知扰动下提升恢复性能并调节身体姿态。采用分层模型预测控制架构，包含高层非线性MPC、姿态感知步频自适应规划器和底层凸型步态MPC。各规划器预测质心（CoM）状态轨迹，评估潜在失稳与姿态偏差前兆。仿真显示，相较基线方法，平均最大可恢复冲量提升131%；硬件实验中，恢复步态启动时间提前125毫秒。同时在0.2弧度扰动下，有效降低身体姿态变化，显著改善恢复性能。
原文摘要 · Abstract (English)
Current humanoid push-recovery strategies often use whole-body motion, yet they tend to overlook posture regulation. For instance, in 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 body attitude change under 0.2 rad.
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