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NVIDIA & University of Southern California:

DiSECt – a differentiable simulation engine for autonomous robotic cutting

Robotics researchers from NVIDIA and the University of Southern California presented their work at the 2021 Robotics: Science and Systems (RSS) conference. It was named DiSECt: the first differentiable simulator for robotic cutting. The simulator accurately predicts the forces acting on a knife as it presses and slices through natural soft materials, such as fruits and vegetables.

The process of cutting with feedback requires adaptation to stiffness of the objects, applied force during the cut, and often a sawing motion to cut through. To achieve this, researchers use a slew of techniques which leverage feedback to guide the controller adaptation. However, fluid controller adaptation requires very careful parameter tuning for each instance of the same problem. While these techniques are successful in industrial settings, no two cucumbers (or tomatoes) are the same, rendering these family of algorithms ineffective in a more generic setting.

DiSECt implements the commonly used Finite Element Method to simulate deformable materials, such as foodstuffs. The object to be cut is represented by a 3D mesh, consisting of tetrahedral elements. Along the cutting surface, the mesh is sliced following the Virtual Node Algorithm. The virtual nodes add extra degrees of freedom to accurately simulate the contact dynamics of the knife when it presses and slices through the mesh.

Next, DiSECt inserts springs connecting the virtual nodes on either side of the cutting surface. These cutting springs allow us to simulate damage mechanics and crack propagation in a continuous manner, by weakening them in proportion to the contact force the knife exerts on the mesh.


Source: developer.nvidia.com


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