Model learning for switching linear systems with autonomous mode transitions

We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate expectation-maximization to learn the maximum- likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.