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[2602.14473] Learning Transferability: A Two-Stage Reinforcement Learning Approach for Enhancing Quadruped Robots' Performance in U-Shaped Stair Climbing
Summary
This article presents a two-stage reinforcement learning approach to enhance the performance of quadruped robots in climbing U-shaped stairs, demonstrating significant transferability of learned policies across different stair types.
Why It Matters
As quadruped robots are increasingly used in construction and other sectors, improving their ability to navigate complex environments like staircases is crucial. This research could lead to more efficient robotic systems that can adapt to various terrains, enhancing their utility in real-world applications.
Key Takeaways
- The study introduces a two-stage reinforcement learning method for quadruped robots.
- Successful implementation allows robots to climb U-shaped stairs autonomously.
- The approach demonstrates the transferability of learned policies to various stair configurations.
Computer Science > Robotics arXiv:2602.14473 (cs) [Submitted on 16 Feb 2026] Title: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 View a PDF of the paper titled Learning Transferability: A Two-Stage Reinforcement Learning Approach for Enhancing Quadruped Robots' Performance in U-Shaped Stair Climbing, by Baixiao Huang and 2 other authors View PDF 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. Comments:...
