Achieving robust locomotion on complex terrains remains a challenge due to high dimensional control and environmental uncertainties. This paper introduces a teacher prior framework based on the teacher student paradigm, integrating imitation and auxiliary task learning to improve learning efficiency and generalization. Unlike traditional paradigms that strongly rely on encoder-based state embeddings, our framework decouples the network design, simplifying the policy network and deployment. A high performance teacher policy is first trained using privileged information to acquire generalizable motion skills. The teacher's motion distribution is transferred to the student policy, which relies only on noisy proprioceptive data, via a generative adversarial mechanism to mitigate performance degradation caused by distributional shifts. Additionally, auxiliary task learning enhances the student policy's feature representation, speeding up convergence and improving adaptability to varying terrains. The framework is validated on a humanoid robot, showing a great improvement in locomotion stability on dynamic terrains and significant reductions in development costs. This work provides a practical solution for deploying robust locomotion strategies in humanoid robots.

本文解决了机器人在复杂地形上运动稳定性不足的问题，提出了一种基于教师-学生范式的教师先验框架，通过模仿学习和辅助任务学习提高学习效率和泛化能力。研究结果表明，该框架显著提高了人形机器人在动态地形上的运动稳定性，并降低了开发成本，为人形机器人移动策略的实际应用提供了有效解决方案。

教师运动先验：增强机器人在复杂地形上的运动能力