Let Humanoids Hike! Integrative Skill Development on Complex Trails

Kwan-Yee Lin, Stella X. Yu; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 22498-22507

Hiking on complex trails demands balance, agility, and adaptive decision-making over unpredictable terrain. Current humanoid research remains fragmented and inadequate for hiking: locomotion focuses on motor skills without long-term goals or situational awareness, while semantic navigation overlooks real-world embodiment and local terrain variability. We propose training humanoids to hike on complex trails, fostering integrative skill development across visual perception, decision making, and motor execution. We develop LEGO-H, a learning framework that enables a humanoid with vision to hike complex trails independently. It has two key innovations. (1) A Temporal Vision Transformer anticipates future steps to guide locomotion, unifying local movement and goal-directed navigation. (2) Latent representations of joint movement patterns combined with hierarchical metric learning allow smooth policy transfer from privileged training to real-world training. These techniques enable LEGO-H to handle diverse physical and environmental challenges without relying on predefined motion patterns. Experiments on diverse simulated hiking trails and humanoids with different morphologies demonstrate LEGO-H's robustness and versatility, establishing a strong foundation for future humanoid development.