无需轨迹分割，端到端学习机器人步态切换与运动控制
Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding
- 基于离散时间混合自动机框架，直接识别模式切换
- 实测四足机器人可稳定完成滑板任务，模式切换符合直觉
- 适合研究复杂运动控制的机器人学者与工程师
混合动力系统包含连续动态和离散模式切换，适用于模拟四足机器人行走等任务。传统模型方法依赖预设步态，而无模型方法缺乏显式切换知识。现有方法通过分割轨迹识别离散模式，但无轨迹标签或分割条件下学习高维刚体动力学仍具挑战。本文提出离散时间混合自动机学习（DHAL）框架，可在不进行轨迹分割或事件函数学习的情况下，实现模式识别与执行。我们将该框架嵌入强化学习流程，引入β策略分布与多评判器架构，以建模接触引导运动，以四足机器人滑板任务为例。通过充分的实地测试验证，方法展现出鲁棒性能，且模式识别结果与人类直觉一致。
原文摘要 · Abstract (English)
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. This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework to identify and execute mode-switching without trajectory segmentation or event function learning. Besides, we embedded it in reinforcement learning pipeline and 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 sufficient real-world tests, demonstrating robust performance and mode identification consistent with human intuition in hybrid dynamical systems.
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