We demonstrate a hybrid approach to autonomous flipper control, focusing on a
fusion of hard-coded and learned knowledge. The result is a sample-efficient and
modifiable control structure that can be used in conjunction with a
mapping/navigation stack. The backbone of the control policy is formulated as a
state machine whose states define various flipper action templates and local
control behaviors. It is also used as an interface that facilitates the
gathering of demonstrations to train the transitions of the state machine. We
propose a soft-differentiable state machine neural network that mitigates the
shortcomings of its naively implemented counterpart and improves over a
multi-layer perceptron baseline in the task of state-transition classification.

We show that by training on several minutes of user-gathered demonstrations in
simulation, our approach is capable of a zero-shot domain transfer to a wide
range of obstacles on a similar real robotic platform. Our results show a
considerable increase in performance over a previous competing approach in
several essential criteria. A subset of this work was successfully used in the
Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge to
alleviate the operator of manual flipper control. We autonomously traversed
stairs and other obstacles, improving map coverage.