Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion

2026-05-10

AI Summary

This work addresses the challenges humanoids face when climbing stairs in real-world environments—namely abrupt geometric transitions, height sensitivity, and perceptual uncertainty—which limit the generalization of existing methods. The authors propose a reinforcement learning strategy conditioned on explicit geometric parameters, directly extracting interpretable features such as step height, depth, and yaw angle to modulate a PPO-based locomotion controller. By eschewing implicit terrain representations in favor of compact, interpretable geometric priors, the approach significantly enhances generalization across diverse stair structures and enables proactive gait adaptation. Simulations demonstrate effective out-of-distribution generalization to unseen step heights, and real-world experiments on the Unitree G1 robot achieve robust indoor and outdoor stair climbing, including uninterrupted ascension of 33 consecutive steps outdoors without failure.

Abstract

Robust humanoid stair climbing remains challenging due to geometric discontinuities, sensitivity to step height variations, and perception uncertainty in real-world environments. Existing learning-based locomotion policies often rely on implicit terrain representations or blind proprioceptive feedback, limiting their ability to generalize across varying stair geometries and to anticipate required gait adjustments. This paper proposes an explicit stair geometry conditioning framework for robust humanoid stair climbing. Instead of encoding terrain as high-dimensional latent features, we extract a compact set of interpretable geometric parameters, including step height, step depth, and current yaw angle relative to the robot heading. These explicit stair parameters directly condition a Proximal Policy Optimization (PPO)-based locomotion policy, enabling proactive modulation of swing-foot clearance and stride characteristics according to stair structure. Simulation experiments demonstrate improved generalization across unseen stair heights beyond the training distribution. Real-world experiments on the Unitree G1 humanoid validate reliable indoor and outdoor stair traversal. In challenging outdoor scenarios, the robot successfully ascends 33 consecutive steps without failure, demonstrating robustness and practical deployability.

Problem

• humanoid locomotion
• stair climbing
• geometric discontinuities
• perception uncertainty
• step height variation

Innovation

• explicit geometry conditioning
• humanoid locomotion
• stair climbing
• generalization
• PPO-based policy