Teacher Motion Priors：增强机器人在挑战性地形上的运动能力
Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain
AI总结：
本文提出基于师生范式的 Teacher Prior Framework，通过模仿与辅助任务学习，并利用生成对抗机制将特权信息训练的教师运动分布迁移至仅依赖带噪声本体感受的学生策略，显著提升了人形机器人在挑战性地形上的运动稳定性并降低了开发成本。
摘要：
由于高维控制和环境不确定性，在复杂地形上实现稳健运动仍是一项挑战。本文提出了一种基于 teacher student paradigm 的 teacher prior framework，结合模仿学习与辅助任务学习，以提升学习效率与泛化能力。与强烈依赖基于编码器的状态嵌入的传统范式不同，我们的框架解耦了网络设计，简化了策略网络与部署过程。首先利用特权信息训练高性能的 teacher policy，以获取可泛化的运动技能。通过生成对抗机制，将 teacher 的运动分布迁移至仅依赖带噪声本体感受数据的 student policy，以缓解由分布偏移导致的性能下降。此外，辅助任务学习增强了 student policy 的特征表示，加快了收敛速度并提升了其对不同地形的适应性。该框架在人形机器人上得到验证，显示出动态地形上运动稳定性的极大提升，并显著降低了开发成本。本研究为在人形机器人中部署稳健运动策略提供了实用方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
