A Self-Supervised Learning Framework for Soft Robot Proprioception

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A novel self-supervised learning framework enhances proprioception in soft robots, suggesting efficiencies in sensor data usage.

The proposed self-supervised learning framework improves proprioception in soft robots, reducing the need for annotated data significantly.
Validation shows our method requires only about 1/20 of the typical annotated samples to outperform fully supervised approaches.
Machine learning techniques are employed to leverage vast unannotated sensor data for effective proprioceptive insights.
This framework can enhance soft robot applications in unstructured environments, where safety and adaptability are crucial.

Hu et al. (2025) studied this question.

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