离散时间混合自动机学习：足式运动与滑板操控的融合

Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding

March 3, 2025

作者: Hang Liu, Sangli Teng, Ben Liu, Wei Zhang, Maani Ghaffari

cs.AI

摘要

本文提出离散时间混合自动机学习（DHAL）框架，该框架利用在线策略强化学习来识别和执行模式切换，而无需轨迹分割或事件函数学习。混合动力系统包含连续流和离散模式切换，能够模拟如足式机器人运动等机器人任务。基于模型的方法通常依赖于预定义的步态，而无模型方法则缺乏明确的模式切换知识。现有方法通过分割识别离散模式后再回归连续流，但在没有轨迹标签或分割的情况下学习高维复杂刚体动力学仍是一个具有挑战性的开放性问题。我们的方法结合了贝塔策略分布和多批评器架构，以建模接触引导的运动，并以具有挑战性的四足机器人滑板任务为例。我们通过仿真和实际测试验证了该方法，展示了其在混合动力系统中的稳健性能。

English

This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework using on-policy Reinforcement Learning to identify and execute mode-switching without trajectory segmentation or event function learning. Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods usually depend on predefined gaits, while model-free approaches lack explicit mode-switching knowledge. Current methods identify discrete modes via segmentation before regressing continuous flow, but learning high-dimensional complex rigid body dynamics without trajectory labels or segmentation is a challenging open problem. Our approach incorporates a beta policy distribution and a multi-critic architecture to model contact-guided motions, exemplified by a challenging quadrupedal robot skateboard task. We validate our method through simulations and real-world tests, demonstrating robust performance in hybrid dynamical systems.