Computational tools for data-driven design of soft robots

We report a numerical simulation framework for the mechanics of soft robots comprised of slender structures, for application in data-driven structural design, material selection, and adaptive control. Owing to prohibitive computational cost, soft robots are often designed solely based on empirical laws through a tedious trial-and-error process with no quantitative guideline. Inspired by fast and efficient modeling of hair and clothes in the animation industry, we adapt algorithms for physically-based simulations from computer graphics. We extend the Discrete Elastic Rods method to develop a simulator for a wide class of robots comprised of multiple slender structures (rods or shells) with compliant joints. In parallel with computation, we perform experiments with biomimetic robots composed of soft thermal actuators and confirm the validity of the simulation tool. Given the large number of parameters and high degree of nonlinearity associated with the performance and functionality of soft robots, emergent machine learning techniques offer a promising avenue for their computational design and optimization. The robustness and speed of our simulation can enable data-driven analysis of a broad range of smart programmable structures beyond soft robots.