学会教导：面向多样地形人形机器人运动的高样本效率特权学习
Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
AI总结：
本文提出单阶段训练框架Learn to Teach (L2T)，统一教师与学生策略学习，通过共享动力学回收仿真样本以降低样本复杂度，在Digit机器人上实现零样本sim-to-real迁移并在12+种地形稳健运动。
摘要：
人形机器人为工业和服务应用带来了变革性能力。尽管强化学习（RL）的最新进展在运动、操作和导航方面取得了令人瞩目的成果，但现有方法通常需要海量仿真样本来应对真实世界的变异性。本研究提出了一种新型单阶段训练框架——Learn to Teach (L2T)，统一了教师与学生策略的学习。该方法通过共享动力学回收仿真器样本并同步学习轨迹，在实现最先进性能的同时，显著降低了样本复杂度和训练时间。此外，我们通过在Digit机器人上的大量仿真和硬件测试验证了RL变体（L2T-RL），在无需深度估计模块的情况下，展示了零样本sim-to-real迁移及在12种以上复杂地形上的稳健性能。
英文摘要：
Humanoid robots promise transformative capabilities for industrial and service applications. While recent advances in Reinforcement Learning (RL) yield impressive results in locomotion, manipulation, and navigation, the proposed methods typically require enormous simulation samples to account for real-world variability. This work proposes a novel one-stage training framework-Learn to Teach (L2T)-which unifies teacher and student policy learning. Our approach recycles simulator samples and synchronizes the learning trajectories through shared dynamics, significantly reducing sample complexities and training time while achieving state-of-the-art performance. Furthermore, we validate the RL variant (L2T-RL) through extensive simulations and hardware tests on the Digit robot, demonstrating zero-shot sim-to-real transfer and robust performance over 12+ challenging terrains without depth estimation modules.
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。
