Let Humanoids Hike! Integrative Skill Development on Complex Trails

TL;DR

This work tackles the challenge of enabling humanoid robots to hike complex trails by unifying perception, planning, and motor control into a single learning framework. LEGO-H combines a Temporal Vision Transformer–based local-goal anticipator (TC-ViT), a Privileged Learning pipeline with an oracle motor policy, and a Hierarchical Latent Matching objective to distill robust, embodied hiking behaviors from simulation. The approach demonstrates that integrative, vision-guided policy learning yields versatile, robust locomotion and navigation across trail variations and morphologies, and it enables cross-robot transfer while identifying avenues for real-world deployment and whole-body control. The results position hiking as a productive testbed for embodied autonomy and establish LEGO-H as a strong baseline for future humanoid development in rugged environments.

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) Latent representations of joint movement patterns, combined with hierarchical metric learning - enhance Privileged Learning scheme - enable smooth policy transfer from privileged training to onboard execution. These components allow LEGO-H to handle diverse physical and environmental challenges without relying on predefined motion patterns. Experiments across varied simulated trails and robot morphologies highlight LEGO-H's versatility and robustness, positioning hiking as a compelling testbed for embodied autonomy and LEGO-H as a baseline for future humanoid development.

Figures (15)

• Figure 1: We propose training humanoids to hike complex trails, driving integrative skill development across visual perception, decision-making, and motor execution.Center: The humanoid robot (H1) a) equipped with vision, learns to b) anticipate near-future local goals to guide locomotion along the trail with self-autonomy. Bubble size (large $\rightarrow$ small) indicates anticipated goal direction; color shows temporal order (orange$\rightarrow$green$\rightarrow$forest). Left: Our LEGO-H framework is universal to different humanoid robots (e.g., G1, a smaller robot) to adaptively c) emerge diverse motor skills, and d) develop embodied path exploration strategies to hike on trails with varied terrains and obstacles. Project page: LEGO-H-HumanoidRobotHiking.github.io.
• Figure 2: Hiking requires locomotion versatility, perceptual awareness, and body-aware planning - integrated for the first time in our approach. Prior work considers only subsets of these capabilities (hatched patterns), whereas LEGO-H unifies all three within a single learning framework to enable embodied autonomy.
• Figure 3: LEGO-H framework overview. LEGO-H equips humanoid robots with adaptive hiking skills by integrating navigation $\mathcal{H}$ and locomotion $\mathcal{E}$ in a unified, end-to-end learning framework ($b$). To foster the versatility of motor skills, we train the unified policy via privileged learning from the oracle policy ($a$).
• Figure 4: TC-ViT Architecture. Three key components: a) a goal-orientated temporal transformer encoder for robots cognizing surroundings with the final goal; b) a parallel process on the current depth frame for integrating spatially precise information to reflect the current state c) a recurrent goal adaptation mechanism that integrates visual awareness, goal information, and proprioception.
• Figure 5: Dynamic adjustments of near goal anticipation. Snapshots from left to right show a robot traversing mixed terrains along a trail. TC-ViT does not provide a fixed trajectory that locomotion module must rigidly follow. Instead, it predicts several near-future goals ($g_{1}, g_{2}, g_{3}$), which dynamically adapt to robot's current state, reflecting real-time adjustments to its navigation decisions. Bubble size (large$\rightarrow$ small) represents predicted local navigation direction.