  We explore learning-based approaches for feedback control of a dexterous
five-finger hand performing non-prehensile manipulation. First, we learn local
controllers that are able to perform the task starting at a predefined initial
state. These controllers are constructed using trajectory optimization with
respect to locally-linear time-varying models learned directly from sensor
data. In some cases, we initialize the optimizer with human demonstrations
collected via teleoperation in a virtual environment. We demonstrate that such
controllers can perform the task robustly, both in simulation and on the
physical platform, for a limited range of initial conditions around the trained
starting state. We then consider two interpolation methods for generalizing to
a wider range of initial conditions: deep learning, and nearest neighbors. We
find that nearest neighbors achieve higher performance. Nevertheless, the
neural network has its advantages: it uses only tactile and proprioceptive
feedback but no visual feedback about the object (i.e. it performs the task
blind) and learns a time-invariant policy. In contrast, the nearest neighbors
method switches between time-varying local controllers based on the proximity
of initial object states sensed via motion capture. While both generalization
methods leave room for improvement, our work shows that (i) local
trajectory-based controllers for complex non-prehensile manipulation tasks can
be constructed from surprisingly small amounts of training data, and (ii)
collections of such controllers can be interpolated to form more global
controllers. Results are summarized in the supplementary video:
https://youtu.be/E0wmO6deqjo
