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.

翻译：四足机器人在建筑施工中应用于多种场景。然而，跨越不同室内楼梯的自主攀爬仍是机器狗完成建筑施工任务的主要挑战。在本项目中，我们采用了一种两阶段端到端深度强化学习（RL）方法来优化机器人在U型楼梯上的性能。训练机器狗模型Unitree Go2首先在Isaac Lab的金字塔楼梯地形上训练攀爬楼梯，然后利用习得的策略攀爬U型室内楼梯。本项目探索了使机器狗能够自主攀爬楼梯的端到端RL方法。结果表明：（1）在引入停滞惩罚的情况下，机器狗成功实现了攀爬U型楼梯的目标；（2）在U型楼梯上训练的策略能够迁移部署到直梯、L型梯和螺旋楼梯地形，且其他楼梯模型训练的策略也可迁移部署到U型地形。