用对抗性运动先验让人形机器人学会滑冰步态。
Learning Roller-Skating Motions of Humanoid Robots Based on Adversarial Motion Priors
- 设计对抗性运动先验框架，从真人滑冰数据中学习双步态。
- 在仿真中实现稳定滑行、速度追踪与转向，性能优于基线。
- 适合对机器人运动控制与强化学习感兴趣的读者。
人形机器人滑冰困难在于需协调全身平衡、滚动接触及速度相关姿态调节。本文提出基于对抗性运动先验的强化学习框架，学习两种人形滑冰步态：泵滑（Pump Glide）与推滑（Push Glide）。两种步态数据通过动作捕捉独立采集，并分别重定向至人形机器人。经平滑与重采样后生成参考运动状态，用于对抗性运动先验训练。采用独立的数据集、策略与奖励架构分别训练两个步态。仿真实验评估了步态质量、速度追踪、转向能力及特定奖励项的消融效果。
原文摘要 · Abstract (English)
Humanoid roller-skating is difficult because the robot must coordinate whole-body balance, rolling contacts, and velocity-dependent posture regulation. This paper presents an adversarial motion prior based reinforcement learning framework for two humanoid roller-skating gaits: Pump Glide skating and Push Glide skating. The two gait datasets are collected independently through motion capture and retargeted to the humanoid robot separately. The retargeted data are then smoothed and resampled into reference motion states for AMP training. The two gaits are learned by independent AMP training pipelines with separate reference datasets, separate policies, and independent reward architectures. Simulation experiments are designed to evaluate gait quality, velocity tracking, turning, and gait-specific reward ablations.
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