Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this paper, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to userprovided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces.We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle.

@Article\{TKMOB20,
  author       = "Tapia, Javier and Knoop, Espen and Mutný, Mojmir and Otaduy, Miguel A. and Bächer, Moritz",
  title        = "MakeSense: Automated Sensor Design for Proprioceptive Soft Robots",
  journal      = "Soft Robotics",
  year         = "2020",
  url          = "http://www.gmrv.es/Publications/2020/TKMOB20"
}