Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets
  Skateboarding
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.
67c733280bfefe1ccd904e4b
Hang Liu
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2025-03-04T21:15:33.353Z
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Hang Liu
user
Hang917
67c733280bfefe1ccd904e4c
Sangli Teng
67c733280bfefe1ccd904e4d
Ben Liu
67c733280bfefe1ccd904e4e
Wei Zhang
67c733280bfefe1ccd904e4f
Maani Ghaffari
Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets
  Skateboarding
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.
A framework using on-policy reinforcement learning to identify and execute mode-switching in hybrid dynamical systems without trajectory segmentation or predefined gaits, demonstrated with a quadrupedal robot skateboard task.
Discrete-time Hybrid Automata Learning on-policy Reinforcement Learning beta policy distribution multi-critic architecture hybrid dynamical systems mode-switching contact-guided motions