Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion
This work addresses robust humanoid stair climbing for real-world deployment, offering a practical improvement over implicit terrain representations.
The paper introduces an explicit stair geometry conditioning framework for humanoid locomotion that uses compact geometric parameters (step height, depth, yaw) to condition a PPO-based policy, achieving robust stair climbing. In real-world tests, the Unitree G1 humanoid ascended 33 consecutive outdoor steps without failure.
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.