Omnidirectional Humanoid Locomotion on Stairs via Unsafe Stepping Penalty and Sparse LiDAR Elevation Mapping

TL;DR

A single-stage training framework incorporating a dense unsafe stepping penalty that provides continuous feedback as the foot approaches a hazardous placement is introduced, achieving a near-100\% safe stepping rate on stair terrains in simulation, while maintaining a remarkably high safe stepping rate in real-world deployments.

Abstract

Humanoid robots, characterized by numerous degrees of freedom and a high center of gravity, are inherently unstable. Safe omnidirectional locomotion on stairs requires both omnidirectional terrain perception and reliable foothold selection. Existing methods often rely on forward-facing depth cameras, which create blind zones that restrict omnidirectional mobility. Furthermore, sparse post-contact unsafe stepping penalties lead to low learning efficiency and suboptimal strategies. To realize safe stair-traversal gaits, this paper introduces a single-stage training framework incorporating a dense unsafe stepping penalty that provides continuous feedback as the foot approaches a hazardous placement. To obtain stable and reliable elevation maps, we build a rolling point-cloud mapping system with spatiotemporal confidence decay and a self-protection zone mechanism, producing temporally consistent local maps. These maps are further refined by an Edge-Guided Asymmetric U-Net (EGAU), which mitigates reconstruction distortion caused by sparse LiDAR returns on stair risers. Simulation and real-robot experiments show that the proposed method achieves a near-100\% safe stepping rate on stair terrains in simulation, while maintaining a remarkably high safe stepping rate in real-world deployments. Furthermore, it completes a continuous long-distance walking test on complex outdoor terrains, demonstrating reliable sim-to-real transfer and long-term stability.

Figures (6)

• Figure 1: Overview of the proposed omnidirectional perceptive locomotion framework deployed on the Unitree G1 humanoid robot. The robot safely performs omnidirectional stair traversal in both indoor and outdoor settings, including forward ascent (a), (c), lateral ascent (b), and backward ascent (d). Orange arrows indicate the direction of motion, and the teal overlay on the stairs illustrates the real-time reconstructed elevation map.
• Figure 2: Overview of the proposed framework. (A) Simulation training: an MLP and a CNN extract proprioceptive and perceptive features, respectively, which are then concatenated. Meanwhile, an unsafe foot penalty function guides the policy to learn safe stair-traversal gaits. (B) Real-world deployment: the trained policy network is directly transferred to the real robot, with a point-cloud mapping and edge-guided elevation map module providing stable and accurate elevation map inputs.
• Figure 3: Illustration of the proposed dense unsafe stepping penalty. The left panel depicts the foot-collision penalty computation, and the right panel depicts the edge-stepping penalty computation.
• Figure 4: Illustration of the spatiotemporal rolling mapping and the protection zone mechanism. The colors of the point cloud indicate temporal confidence levels. The red conical region represents the physical blind zone of the LiDAR, and the light blue cylinder denotes the self-protection zone $\mathcal{Z}_{\text{safe}}$ constructed beneath the base of the robot.
• Figure 5: Comprehensive performance comparison in simulation. (a) Safe stepping rate as a function of stair height for each method. The top row corresponds to forward, backward, and lateral stair ascent, while the bottom row shows the corresponding descent results. (b) Terrain curriculum level progression over the course of training. (c) Forward linear velocity tracking when ascending 15-cm stairs with a commanded velocity of 0.7 m/s.