用强化学习让轮腿机器人自适应地形，省电又灵活
ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots
- 用强化学习自主协调轮子与腿的运动，不依赖预设步态
- 在平坦地、台阶、草地等多地形下均实现稳定行走与驾驶
- 能从感知数据预测环境状态，适合复杂未知地形应用
轮腿混合机器人因其兼具腿部敏捷性与轮式高效性的优势，近年来受到广泛关注。然而，其全身控制在混合运动中仍具挑战性。本文提出ATRos，一个基于强化学习的混合运动框架，用于实现轮腿机器人的行走与驾驶复合运动。该规划器无需预设步态，可智能协调轮子与腿部的同步动作，从而提升地形适应性和能量效率。基于强化学习技术，该方法构建了预测策略网络，利用本体感知信息估计外部环境状态，并将结果输入演员-评论家网络以生成最优关节指令。通过仿真和真实世界实验，在平坦地面、台阶和草地等多种地形上验证了该框架的可行性。混合运动表现稳健，展现出优异的泛化能力。
原文摘要 · Abstract (English)
Hybrid locomotion of wheeled-legged robots has recently attracted increasing attention due to their advantages of combining the agility of legged locomotion and the efficiency of wheeled motion. But along with expanded performance, the whole-body control of wheeled-legged robots remains challenging for hybrid locomotion. In this paper, we present ATRos, a reinforcement learning (RL)-based hybrid locomotion framework to achieve hybrid walking-driving motions on the wheeled-legged robot. Without giving predefined gait patterns, our planner aims to intelligently coordinate simultaneous wheel and leg movements, thereby achieving improved terrain adaptability and improved energy efficiency. Based on RL techniques, our approach constructs a prediction policy network that could estimate external environmental states from proprioceptive sensory information, and the outputs are then fed into an actor critic network to produce optimal joint commands. The feasibility of the proposed framework is validated through both simulations and real-world experiments across diverse terrains, including flat ground, stairs, and grassy surfaces. The hybrid locomotion framework shows robust performance over various unseen terrains, highlighting its generalization capability.
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