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

Soft Multicopter Control using Neural Dynamics Identification