A Self-Supervised Learning Framework for Soft Robot Proprioception

The inherent compliant nature of soft robots can offer remarkable advantages over their rigid counterparts in terms of safety to human users and adaptability in unstructured environments. However, this feature also magnifies the complexity of their bodies, rendering their proprioception, and hence their control, extremely challenging. Given this intricacy, machine learning is a potent candidate for extracting proprioceptive insights from sensor data due to its proven capabilities in tackling analogous issues in computer vision (CV) and natural language processing (NLP). Recently, key aspects of soft robot proprioception have been addressed via learning-based techniques, but most of these are rooted in the supervised learning (SL) paradigm. This typically requires collecting a large number of costly annotated training samples, thereby constraining its widespread and speedy adoption in real-world applications. To mitigate this limitation, we propose a self-SL framework for soft robot proprioception. Our method utilizes vast unannotated data for network pretraining by self-SL. Then, the pretrained model is fine-tuned with a limited set of annotated samples by SL. We validate the proposed method's efficacy on a high-resolution 3-D morphological reconstruction task using a publicly available dataset. Remarkably, our approach is shown to necessitate only about 1/20 of annotated samples to achieve better performance than the fully supervised method.