用神经网络动态调整步频，让机器人更灵活地走不平路。
Gait-Net-augmented Implicit Kino-dynamic MPC for Dynamic Variable-frequency Humanoid Locomotion over Discrete Terrains
- 用轻量级网络预测最优步长和步频，简化控制优化。
- 仅需一步地形预览即可实现在不平地形上稳定行走。
- 适合需要快速适应复杂地形的仿人机器人研究者。
基于简化模型的最优控制方法在动态步行中难以同时调整步长与步频，因其依赖固定时间离散化，对扰动响应慢，导致复杂环境下性能不佳。本文提出一种融合Gait-Net的隐式运动学动力学模型预测控制（MPC）方法，可同步优化步位、步频和接触力，实现自然变频行走。核心是轻量级步频网络（Gait-Net），根据可变MPC采样周期确定理想步长，将步频优化转化为参数调节。同时，在每次迭代中通过局部解更新空间参考轨迹，将运动学约束融入轨迹设计。在高保真仿真和小型仿人机器人硬件上验证，仅需一步地形预览即可实现三维离散地形上的变频行走。
原文摘要 · Abstract (English)
Reduced-order-model-based optimal control techniques for humanoid locomotion struggle to adapt step duration and placement simultaneously in dynamic walking gaits due to their reliance on fixed-time discretization, which limits responsiveness to various disturbances and results in suboptimal performance in challenging conditions. In this work, we propose a Gait-Net-augmented implicit kino-dynamic model-predictive control (MPC) to simultaneously optimize step location, step duration, and contact forces for natural variable-frequency locomotion. The proposed method incorporates a Gait-Net-augmented Sequential Convex MPC algorithm to solve multi-linearly constrained variables by iterative quadratic programs. At its core, a lightweight Gait-frequency Network (Gait-Net) determines the preferred step duration in terms of variable MPC sampling times, simplifying step duration optimization to the parameter level. Additionally, it enhances and updates the spatial reference trajectory within each sequential iteration by incorporating local solutions, allowing the projection of kinematic constraints to the design of reference trajectories. We validate the proposed algorithm in high-fidelity simulations and on small-size humanoid hardware, demonstrating its capability for variable-frequency and 3-D discrete terrain locomotion with only a one-step preview of terrain data.
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