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arXiv · 2503.01842

Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding

Hang Liu·Sangli Teng·Ben Liu·Wei Zhang·Maani Ghaffari
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Abstract

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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Hang Liu, Sangli Teng, Ben Liu, Wei Zhang, Maani Ghaffari. 2025-03-03. Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding. https://arxiv.org/abs/2503.01842

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