Outdoor Environments
Robust walking in outdoor campus environment
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.
Robust walking in outdoor campus environment
Stable walking on flat concrete surface with strong wind
Navigation through indoor corridors and spaces
Traversing rocky terrain with uneven surfaces
Walking on loose sand surface
Stable walking on gravel pavement
Walking on natural grass surface
Smooth turning behavior
Forward walking on football field
Turning on football field
Recovery from forward push
Recovery from backward push
Recovery from pulling force
Demonstrating the slippery surface
Walking on slippery surface with L2T policy
Walking on slippery surface with the company controller from Agility Robotics
Carrying payload while walking
Walking on elevated surface
@misc{wu2025learnteachsampleefficientprivileged,
title={Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains},
author={Feiyang Wu and Xavier Nal and Jaehwi Jang and Wei Zhu and Zhaoyuan Gu and Anqi Wu and Ye Zhao},
year={2025},
eprint={2402.06783},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2402.06783},
}