title: Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets
    Skateboarding
summary: 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_authors._id: 67c733280bfefe1ccd904e4b
paper_authors.name: Hang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T21:15:33.353Z
paper_authors._id: 67ba6ecc3d75e6192e1a18e6
paper_authors.avatarUrl: /avatars/ca8a31c00fa799d5da19dbc73e1d5faf.svg
paper_authors.fullname: Hang Liu
paper_authors.type: user
paper_authors.user: Hang917
paper_authors._id: 67c733280bfefe1ccd904e4c
paper_authors.name: Sangli Teng
paper_authors._id: 67c733280bfefe1ccd904e4d
paper_authors.name: Ben Liu
paper_authors._id: 67c733280bfefe1ccd904e4e
paper_authors.name: Wei Zhang
paper_authors._id: 67c733280bfefe1ccd904e4f
paper_authors.name: Maani Ghaffari
paper_title: Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets
    Skateboarding
paper_summary: 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_ai_summary: 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.
paper_ai_keywords: Discrete-time Hybrid Automata Learning on-policy Reinforcement Learning beta policy distribution multi-critic architecture hybrid dynamical systems mode-switching contact-guided motions