E-TRoll: Tactile Sensing and Classification via A Simple Robotic Gripper for Extended Rolling Manipulations

TL;DR

This paper introduces E-TRoll, a simple robotic gripper with adaptive control for efficient tactile object recognition through extended rolling manipulations, achieving high accuracy with reduced data complexity.

Contribution

The work presents a novel adaptive robotic hand design and a feature extraction algorithm that significantly improve tactile object classification efficiency and accuracy.

Findings

Over 60% increase in tactile data exposure area during rolling

Reduced data dimensionality from 10x1500 to 22 features

Achieved 95.6% cross-validation accuracy in object shape recognition

Abstract

Robotic tactile sensing provides a method of recognizing objects and their properties where vision fails. Prior work on tactile perception in robotic manipulation has frequently focused on exploratory procedures (EPs). However, the also-human-inspired technique of in-hand-manipulation can glean rich data in a fraction of the time of EPs. We propose a simple 3-DOF robotic hand design, optimized for object rolling tasks via a variable-width palm and associated control system. This system dynamically adjusts the distance between the finger bases in response to object behavior. Compared to fixed finger bases, this technique significantly increases the area of the object that is exposed to finger-mounted tactile arrays during a single rolling motion (an increase of over 60% was observed for a cylinder with a 30-millimeter diameter). In addition, this paper presents a feature extraction algorithm for the collected spatiotemporal dataset, which focuses on object corner identification, analysis, and compact representation. This technique drastically reduces the dimensionality of each data sample from 10 x 1500 time series data to 80 features, which was further reduced by Principal Component Analysis (PCA) to 22 components. An ensemble subspace k-nearest neighbors (KNN) classification model was trained with 90 observations on rolling three different geometric objects, resulting in a three-fold cross-validation accuracy of 95.6% for object shape recognition.