Object Classification Utilizing Neuromorphic Proprioceptive Signals in Active Exploration: Validated on a Soft Anthropomorphic Hand

TL;DR

This paper presents a neuromorphic proprioceptive system integrated with a soft robotic hand that improves object classification accuracy during active exploration, advancing haptic feedback and prosthetic control.

Contribution

It introduces a novel neuromorphic proprioception platform combining a soft anthropomorphic hand, flexible sensors, and a spiking neural network classifier for object recognition.

Findings

Classifier outperforms existing methods in early exploration stages

System achieves high accuracy on YCB benchmark objects

Neuromorphic signals enhance haptic perception

Abstract

Proprioception, a key sensory modality in haptic perception, plays a vital role in perceiving the 3D structure of objects by providing feedback on the position and movement of body parts. The restoration of proprioceptive sensation is crucial for enabling in-hand manipulation and natural control in the prosthetic hand. Despite its importance, proprioceptive sensation is relatively unexplored in an artificial system. In this work, we introduce a novel platform that integrates a soft anthropomorphic robot hand (QB SoftHand) with flexible proprioceptive sensors and a classifier that utilizes a hybrid spiking neural network with different types of spiking neurons to interpret neuromorphic proprioceptive signals encoded by a biological muscle spindle model. The encoding scheme and the classifier are implemented and tested on the datasets we collected in the active exploration of ten objects from the YCB benchmark. Our results indicate that the classifier achieves more accurate inferences than existing learning approaches, especially in the early stage of the exploration. This system holds the potential for development in the areas of haptic feedback and neural prosthetics.