  Hybrid systems, such as bipedal walkers, are challenging to control because
of discontinuities in their nonlinear dynamics. Little can be predicted about
the systems' evolution without modeling the guard conditions that govern
transitions between hybrid modes, so even systems with reliable state sensing
can be difficult to control. We propose an algorithm that allows for
determining the hybrid mode of a system in real-time using data-driven
analysis. The algorithm is used with data-driven dynamics identification to
enable model predictive control based entirely on data. Two examples---a
simulated hopper and experimental data from a bipedal walker---are used. In the
context of the first example, we are able to closely approximate the dynamics
of a hybrid SLIP model and then successfully use them for control in
simulation. In the second example, we demonstrate gait partitioning of human
walking data, accurately differentiating between stance and swing, as well as
selected subphases of swing. We identify contact events, such as heel strike
and toe-off, without a contact sensor using only kinematics data from the knee
and hip joints, which could be particularly useful in providing online
assistance during walking. Our algorithm does not assume a predefined gait
structure or gait phase transitions, lending itself to segmentation of both
healthy and pathological gaits. With this flexibility, impairment-specific
rehabilitation strategies or assistance could be designed.
