  Robotic cutting of soft materials is critical for applications such as food
processing, household automation, and surgical manipulation. As in other areas
of robotics, simulators can facilitate controller verification, policy
learning, and dataset generation. Moreover, differentiable simulators can
enable gradient-based optimization, which is invaluable for calibrating
simulation parameters and optimizing controllers. In this work, we present
DiSECt: the first differentiable simulator for cutting soft materials. The
simulator augments the finite element method (FEM) with a continuous contact
model based on signed distance fields (SDF), as well as a continuous damage
model that inserts springs on opposite sides of the cutting plane and allows
them to weaken until zero stiffness, enabling crack formation. Through various
experiments, we evaluate the performance of the simulator. We first show that
the simulator can be calibrated to match resultant forces and deformation
fields from a state-of-the-art commercial solver and real-world cutting
datasets, with generality across cutting velocities and object instances. We
then show that Bayesian inference can be performed efficiently by leveraging
the differentiability of the simulator, estimating posteriors over hundreds of
parameters in a fraction of the time of derivative-free methods. Next, we
illustrate that control parameters in the simulation can be optimized to
minimize cutting forces via lateral slicing motions. Finally, we conduct
experiments on a real robot arm equipped with a slicing knife to infer
simulation parameters from force measurements. By optimizing the slicing motion
of the knife, we show on fruit cutting scenarios that the average knife force
can be reduced by more than 40% compared to a vertical cutting motion. We
publish code and additional materials on our project website at
https://diff-cutting-sim.github.io.

Robotics;Artificial Intelligence