用预测运动先验让机器人手脚配合更精准，远程操控更稳定。
Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control
- 上肢控制用逆运动学+运动重定向，下肢用强化学习保稳行走
- 用条件变分自编码器学出运动先验，使操作精度显著提升
- 适合需要精细操作与稳定移动的远程人形机器人应用
人形机器人需兼具稳健的下肢行走和精确的上肢操作。现有强化学习方法虽能实现全身协同控制，但高自由度机械臂的精细操作能力不足。本文提出将上肢控制与行走解耦：上肢采用逆运动学与运动重定向实现精确操作，下肢则专注于鲁棒行走。引入预测运动先验（PMP），基于条件变分自编码器（CVAE）训练，有效表征上肢动作。下肢策略以该运动表示为条件进行训练，确保在操作与移动中均保持稳定。实验表明，CVAE特征对系统稳定性至关重要，显著优于端到端强化学习的全身体控方案。结合精准上肢运动与稳健下肢控制，操作员可远程指挥机器人在复杂环境中行走并完成多样化任务。
原文摘要 · Abstract (English)
Humanoid robots require both robust lower-body locomotion and precise upper-body manipulation. While recent Reinforcement Learning (RL) approaches provide whole-body loco-manipulation policies, they lack precise manipulation with high DoF arms. In this paper, we propose decoupling upper-body control from locomotion, using inverse kinematics (IK) and motion retargeting for precise manipulation, while RL focuses on robust lower-body locomotion. We introduce PMP (Predictive Motion Priors), trained with Conditional Variational Autoencoder (CVAE) to effectively represent upper-body motions. The locomotion policy is trained conditioned on this upper-body motion representation, ensuring that the system remains robust with both manipulation and locomotion. We show that CVAE features are crucial for stability and robustness, and significantly outperforms RL-based whole-body control in precise manipulation. With precise upper-body motion and robust lower-body locomotion control, operators can remotely control the humanoid to walk around and explore different environments, while performing diverse manipulation tasks.
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