Learning Transferability: A Two-Stage Reinforcement Learning Approach for Enhancing Quadruped Robots' Performance in U-Shaped Stair Climbing

Baixiao Huang; Baiyu Huang; Yu Hou

cs.ROFeb 16, 2026

Learning Transferability: A Two-Stage Reinforcement Learning Approach for Enhancing Quadruped Robots' Performance in U-Shaped Stair Climbing

Authors: Baixiao Huang, Baiyu Huang, Yu Hou

Organizations: Independent Researcher, 7200 Bollinger Rd, Apt 516, CA 95129. · Independent Researcher, 16506 82nd Pl NE, Kenmore, WA, 98028. · Western New England University, Department of Construction Management, 1215 Wilbraham Rd, Springfield, MA 01119 (corresponding author).

Abstract

Quadruped robots are employed in various scenarios in building construction. However, autonomous stair climbing across different indoor staircases remains a major challenge for robot dogs to complete building construction tasks. In this project, we employed a two-stage end-to-end deep reinforcement learning (RL) approach to optimize a robot's performance on U-shaped stairs. The training robot-dog modality, Unitree Go2, was first trained to climb stairs on Isaac Lab's pyramid-stair terrain, and then to climb a U-shaped indoor staircase using the learned policies. This project explores end-to-end RL methods that enable robot dogs to autonomously climb stairs. The results showed (1) the successful goal reached for robot dogs climbing U-shaped stairs with a stall penalty, and (2) the transferability from the policy trained on U-shaped stairs to deployment on straight, L-shaped, and spiral stair terrains, and transferability from other stair models to deployment on U-shaped terrain.

RL for Legged Locomotion

Reinforcement learning for legged locomotion trains walking robots to move across varied terrain, adapt to disturbances, and carry out tasks.

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Jun 24, 2026J/K move · Enter open · S save

StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots

Xincheng Tang, Youhan Xie, Zhengjie Shu, Wanyu Li+4

Shanghai Jiao Tong University

Sim-to-Real Transfer Terrain-Aware Robot Locomotion RL for Legged Locomotion Quadruped Locomotion

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Climbing hollow stairs remains a challenging problem for quadruped robots due to the high risk of leg trapping, severe depth sparsity, and high-frequency depth-sensing noise. In this paper, we propose StairMaster, a novel three-stage reinforcement learning framework for stable locomotion on such extreme discontinuous terrains. Our architecture integrates a Cross-Attention mechanism to extract structural features from noisy depth data, alongside a Spatial-aware Recurrent Unit (SRU) that maintains robust spatio-temporal memory to mitigate perception blind spots. To bridge the sim-to-real gap in depth perception, we propose a high-fidelity sim-to-real depth sensor modeling pipeline that faithfully replicates real-world sensor artifacts. Additionally, we employ a 3D waypoint-guided active perception reward for proactive sensing, alongside hollow gap kinematic and stair edge penalties to ensure precise foothold placement. We successfully deployed StairMaster on a Unitree Go2 robot, demonstrating its ability to conquer hollow stairs with an unprecedented incline of up to 55

^\circ

through zero-shot transfer. To the best of our knowledge, this is the first RL-based policy to achieve such steep hollow stair climbing in real-world environments. Project Website: https://sivan666666.github.io/StairMaster/.

Abstract

RL for Legged Locomotion

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