Towards integrated tactile sensorimotor control in anthropomorphic soft robotic hands

TL;DR

This paper demonstrates how integrated tactile sensing and control can enable soft robotic hands to perform delicate grasping and manipulation tasks, advancing human-like touch capabilities in robotics.

Contribution

It introduces a novel integrated sensorimotor control framework for soft robotic hands using tactile sensors and neural networks, enhancing autonomous manipulation.

Findings

Successful light contact establishment on unknown objects

Effective edge pose estimation using CNNs

Foundation for human-like tactile control in soft robots

Abstract

In this work, we report on the integrated sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactile sensor based on the human sense of touch. Our focus is how a sense of touch can be used to control an anthropomorphic hand with one degree of actuation, based on an integration that respects the hand's mechanical functionality. We consider: (i) closed-loop tactile control to establish a light contact on an unknown held object, based on the structural similarity with an undeformed tactile image; and (ii) controlling the estimated pose of an edge feature of a held object, using a convolutional neural network approach developed for controlling other sensors in the TacTip family. Overall, this gives a foundation to endow soft robotic hands with human-like touch, with implications for autonomous grasping, manipulation, human-robot interaction and prosthetics. Supplemental video: https://youtu.be/ndsxj659bkQ