用人类步态生成网络+强化学习，让机器人在复杂地形上稳健行走。
Human Imitated Bipedal Locomotion with Frequency Based Gait Generator Network
- 基于人类运动频谱特征设计步态生成网络
- 仅在平地训练却能适应陡坡与粗糙地形
- 轻量级框架，训练成本低，适合真实场景部署
由于系统存在混合动态特性及地形变化，学习类人且鲁棒的双足行走依然困难。本文提出一种轻量级框架，将从人类运动数据中学习到的步态生成网络与近端策略优化（PPO）控制器结合，实现关节力矩控制。尽管训练仅在平坦或轻微倾斜地面进行，所学策略仍可泛化至更陡坡度和粗糙表面。结果表明，将频谱运动先验与深度强化学习结合，为实现自然且鲁棒的双足行走提供了一条可行路径，且训练成本较低。
原文摘要 · Abstract (English)
Learning human-like, robust bipedal walking remains difficult due to hybrid dynamics and terrain variability. We propose a lightweight framework that combines a gait generator network learned from human motion with Proximal Policy Optimization (PPO) controller for torque control. Despite being trained only on flat or mildly sloped ground, the learned policies generalize to steeper ramps and rough surfaces. Results suggest that pairing spectral motion priors with Deep Reinforcement Learning (DRL) offers a practical path toward natural and robust bipedal locomotion with modest training cost.
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