Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding

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

Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding: 3 upvotes on Hugging Face Daily Papers, #25 of 26 papers on 2025-03-05. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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