Robotics: Science and Systems XXI

Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding

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

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. Moreover, we embed it in a reinforcement learning pipeline and incorporate a beta policy distribution and a multicritic 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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Bibtex:

  
@INPROCEEDINGS{LiuH-RSS-25, 
    AUTHOR    = {Hang Liu AND Sangli Teng AND Ben Liu AND Wei Zhang AND Maani Ghaffari}, 
    TITLE     = {{Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2025}, 
    ADDRESS   = {LosAngeles, CA, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2025.XXI.127} 
}