MakeSense: Automated Sensor Design for Proprioceptive Soft Robots
By Javier Tapia, Espen Knoop, Mojmír Mutný, M. Otaduy, Moritz Bächer. 2019
114 citations recorded by the source corpus.
Soft robots have applications in safe human–robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided 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.
Subfields: Biomedical Engineering, Condensed Matter Physics
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In Biomedical Engineering
Citations in the corpus
- Coresets via Bilevel Optimization for Continual Learning and Streaming (2020, methodology): “Closest to our work, bilevel optimization was used in [47] to reweigh samples to overcome training set imbalance or corruption, and sensor selection was approached via bilevel optimization in [51].”