Learning Humanoid Locomotion over Challenging Terrain
<|reference_start|>Learning Humanoid Locomotion over Challenging Terrain: Humanoid robots can, in principle, use their legs to go almost anywhere. Developing controllers capable of traversing diverse terrains, however, remains a considerable challenge. Classical controllers are hard to generalize broadly while the learning-based methods have primarily focused on gentle terrains. Here, we present a learning-based approach for blind humanoid locomotion capable of traversing challenging natural and man-made terrain. Our method uses a transformer model to predict the next action based on the history of proprioceptive observations and actions. The model is first pre-trained on a dataset of flat-ground trajectories with sequence modeling, and then fine-tuned on uneven terrain using reinforcement learning. We evaluate our model on a real humanoid robot across a variety of terrains, including rough, deformable, and sloped surfaces. The model demonstrates robust performance, in-context adaptation, and emergent terrain representations. In real-world case studies, our humanoid robot successfully traversed over 4 miles of hiking trails in Berkeley and climbed some of the steepest streets in San Francisco.<|reference_end|>
@article{radosavovic2024learning,
    title={Learning Humanoid Locomotion over Challenging Terrain},
    author={Ilija Radosavovic, Sarthak Kamat, Trevor Darrell, Jitendra Malik},
    journal={arXiv preprint arXiv:2410.03654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03654},
    primaryClass={cs.RO cs.LG}
}