  Dynamic control of a soft-body robot to deliver complex behaviors with
low-dimensional actuation inputs is challenging. In this paper, we present a
computational approach to automatically generate versatile, underactuated
control policies that drives soft-bodied machines with complicated structures
and nonlinear dynamics. Our target application is focused on the autonomous
control of a soft multicopter, featured by its elastic material components,
non-conventional shapes, and asymmetric rotor layouts, to precisely deliver
compliant deformation and agile locomotion. The central piece of our approach
lies in a lightweight neural surrogate model to identify and predict the
temporal evolution of a set of geometric variables characterizing an elastic
soft body. This physics-based learning model is further integrated into a
Linear Quadratic Regulator (LQR) control loop enhanced by a novel online
fixed-point relinearization scheme to accommodate the dynamic body balance,
allowing an aggressive reduction of the computational overhead caused by the
conventional full-scale sensing-simulation-control workflow. We demonstrate the
efficacy of our approach by generating controllers for a broad spectrum of
customized soft multicopter designs and testing them in a high-fidelity physics
simulation environment. The control algorithm enables the multicopters to
perform a variety of tasks, including hovering, trajectory tracking, cruising
and active deforming.
