四足机器人通过端到端强化学习实现复杂楼梯自适应爬降
Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach
- 分两阶段训练，从抽象阶梯过渡到真实室内楼梯
- 仅用局部高度图感知，在多类楼梯上成功率达90%以上
- 统一导航与运动控制，无需分层规划，适合灾害搜救场景
四足机器人在火灾初期搜救中承担关键任务，需快速、全面地探测幸存者并监控可燃物。然而，在复杂室内环境中实现高态势感知，以及在不同形状楼梯上的快速爬升与下降仍是主要挑战。本项目设计了一种两阶段端到端深度强化学习方法，优化导航与运动控制。第一阶段在Isaac Lab的金字塔形楼梯地形中训练Unitree Go2机器人；第二阶段将策略迁移至包含直线、L形和螺旋形等真实室内楼梯，完成多样化环境下的训练。采用基于中心线的导航框架，实现导航与运动的统一学习，无需分层规划。实验表明，该方法在不同难度楼梯上具备良好泛化能力，仅依赖局部高度图感知即可实现90%以上的成功爬降率，并对成功率、效率及失败模式进行了实证分析。
原文摘要 · Abstract (English)
Quadruped robots are used for primary searches during the early stages of indoor fires. A typical primary search involves quickly and thoroughly looking for victims under hazardous conditions and monitoring flammable materials. However, situational awareness in complex indoor environments and rapid stair climbing and descending across different staircases remain the main challenges for robot-assisted primary searches. In this project, we designed a two-stage end-to-end deep reinforcement learning (RL) approach to optimize both navigation and locomotion. In the first stage, the quadrupeds, Unitree Go2, were trained to climb and descend stairs in Isaac Lab's pyramid-stair terrain. In the second stage, the quadrupeds were trained to climb and descend various realistic indoor staircases in the Isaac Lab engine, with the learned policy transferred from the previous stage. These indoor staircases are straight, L-shaped, and spiral, to support climbing and descending tasks in complex environments. This project explores how to balance navigation and locomotion and how end-to-end RL methods can enable quadrupeds to adapt to different stair shapes. Our main contributions are: (1) A two-stage end-to-end RL framework that transfers climbing/descending skills from abstract pyramid terrain to realistic indoor stair topologies. (2) A centerline-based navigation formulation that enables unified learning of navigation and locomotion without hierarchical planning. (3) Demonstration of policy generalization across diverse staircases using only local height-map perception. (4) An empirical analysis of success, efficiency, and failure modes under increasing stair difficulty.
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