Model-Free Soft-Structure Reconstruction for Proprioception Using Tactile Arrays

Abstract

Continuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this letter, we propose a model-free method for sensorizing tentacle-like continuum soft-structures using an array of spatially arranged capacitive tactile sensors. By using visual tracking, the relationship between the tactile response and the three-dimensional shape of the continuum soft-structure can be learned. A dataset of 15 000 random soft-body postures was used, with recorded camera-tracked positions logged synchronously to the tactile sensor responses. This was used to train a neural network that can predict posture. We show that it is possible to achieve proprioceptive awareness over all three axis of motion in space, reconstructing the body structure and inferring the soft body head's pose with an average accuracy of ≈1 mm in comparison to the visual tracked counterpart. To demonstrate the capabilities of the system, we perform random exploration of environments limiting the work-space of the sensorized robot. We find the method capable to autonomously reconstruct the reachable morphology of the environment without the need of external sensing units.