标题： TAR：通过对比学习实现的四足运动教师对齐表示 显示英文标题

标题： TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion

摘要： 四足运动通过强化学习（RL）通常使用教师-学生范式来解决，其中特权教师指导本体感觉学生策略。 然而，诸如特权教师与仅本体感觉的学生之间的表示不匹配、由于行为克隆导致的协变量偏移以及缺乏可部署的适应性等问题，导致在现实场景中泛化能力较差。 我们提出了通过对比学习对齐教师表示（TAR），一种利用特权信息和自监督对比学习来弥合这一差距的框架。 通过在模拟中使用对比目标将表示对齐到特权教师，我们的学生策略学习到结构化的潜在空间，并在分布外（OOD）场景中表现出稳健的泛化能力，超过了完全特权的“教师”。 结果表明，与最先进的基线相比，训练速度提高了2倍，以达到最佳性能。 与现有方法相比，OOD场景的泛化能力平均提高了40%。 此外，TAR在部署期间无缝过渡到学习，而无需特权状态，为样本高效、自适应的运动设定了新基准，并在现实场景中实现了持续微调。 开源代码和视频可在 https://amrmousa.com/TARLoco/ 获取。 显示英文摘要

摘要： Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy. However, key challenges such as representation misalignment between privileged teacher and proprioceptive-only student, covariate shift due to behavioral cloning, and lack of deployable adaptation; lead to poor generalization in real-world scenarios. We propose Teacher-Aligned Representations via Contrastive Learning (TAR), a framework that leverages privileged information with self-supervised contrastive learning to bridge this gap. By aligning representations to a privileged teacher in simulation via contrastive objectives, our student policy learns structured latent spaces and exhibits robust generalization to Out-of-Distribution (OOD) scenarios, surpassing the fully privileged "Teacher". Results showed accelerated training by 2x compared to state-of-the-art baselines to achieve peak performance. OOD scenarios showed better generalization by 40% on average compared to existing methods. Moreover, TAR transitions seamlessly into learning during deployment without requiring privileged states, setting a new benchmark in sample-efficient, adaptive locomotion and enabling continual fine-tuning in real-world scenarios. Open-source code and videos are available at https://amrmousa.com/TARLoco/.