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InfoResearchPeer-reviewed
A Self-Supervised Learning Framework for Soft Robot Proprioception
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Summary
The authors propose a self-supervised learning framework for soft robot proprioception, a task made hard by the complex, compliant bodies of soft robots. The method pretrains a network on abundant unannotated sensor data, then fine-tunes it with a small set of annotated samples. Validated on a high-resolution 3-D morphological reconstruction task using a publicly available dataset, the approach needs only about 1/20 of the annotated samples to outperform a fully supervised method.