  Switching physical systems are ubiquitous in modern control applications, for
instance, locomotion behavior of robots and animals, power converters with
switches and diodes. The dynamics and switching conditions are often hard to
obtain or even inaccessible in case of a-priori unknown environments and
nonlinear components. Black-box neural networks can learn to approximately
represent switching dynamics, but typically require a large amount of data,
neglect the underlying axioms of physics, and lack of uncertainty
quantification. We propose a Gaussian process based learning approach enhanced
by switching Port-Hamiltonian systems (GP-SPHS) to learn physical plausible
system dynamics and identify the switching condition. The Bayesian nature of
Gaussian processes uses collected data to form a distribution over all possible
switching policies and dynamics that allows for uncertainty quantification.
Furthermore, the proposed approach preserves the compositional nature of
Port-Hamiltonian systems. A simulation with a hopping robot validates the
effectiveness of the proposed approach.
