Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets
  Skateboarding
Authors: name: Hang Liu, name: Sangli Teng, name: Ben Liu, name: Wei Zhang, name: Maani Ghaffari

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.