Let Humanoids Hike! Integrative Skill Development on Complex Trails
Kwan-Yee Lin, Stella X.Yu

TL;DR
This paper introduces LEGO-H, a comprehensive learning framework enabling humanoid robots to autonomously hike complex trails by integrating perception, decision-making, and motor skills, advancing embodied autonomy research.
Contribution
The paper presents a novel hierarchical reinforcement learning approach with vision transformers and latent movement representations for integrated trail hiking in humanoids.
Findings
LEGO-H demonstrates robustness across diverse simulated trails.
The framework effectively transfers policies from training to real-world scenarios.
Humanoids achieve adaptive, goal-directed navigation on complex terrains.
Abstract
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, driving integrative skill development across visual perception, decision making, and motor execution. We develop a learning framework, LEGO-H, that enables a vision-equipped humanoid robot to hike complex trails autonomously. We introduce two technical innovations: 1) A temporal vision transformer variant - tailored into Hierarchical Reinforcement Learning framework - anticipates future local goals to guide movement, seamlessly integrating locomotion with goal-directed navigation. 2)…
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Taxonomy
TopicsRobotic Locomotion and Control · Zebrafish Biomedical Research Applications · Action Observation and Synchronization
MethodsLayer Normalization · Softmax · Residual Connection · Linear Layer · Multi-Head Attention · Dense Connections · Attention Is All You Need · Vision Transformer
