Supervised Autoencoder Joint Learning on Heterogeneous Tactile Sensory Data: Improving Material Classification Performance

TLDR

Touch provides robots with rich multimodal sensory information that enhances perception, yet heterogeneous sensor formats hinder large‑scale learning of tactile data. The study examines whether data from two distinct tactile sensors can be jointly learned to classify common materials. A supervised recurrent autoencoder is trained on sliding‑motion recordings from iCub RoboSkin and SynTouch BioTac sensors across 20 material textures to perform joint material classification. Joint learning yields higher accuracy and learning efficiency than training each dataset separately, indicating that shared datasets across sensors can be effectively leveraged.

Abstract

The sense of touch is an essential sensing modality for a robot to interact with the environment as it provides rich and multimodal sensory information upon contact. It enriches the perceptual understanding of the environment and closes the loop for action generation. One fundamental area of perception that touch dominates over other sensing modalities, is the understanding of the materials that it interacts with, for example, glass versus plastic. However, unlike the senses of vision and audition which have standardized data format, the format for tactile data is vastly dictated by the sensor manufacturer, which makes it difficult for large-scale learning on data collected from heterogeneous sensors, limiting the usefulness of publicly available tactile datasets. This paper investigates the joint learnability of data collected from two tactile sensors performing a touch sequence on some common materials. We propose a supervised recurrent autoencoder framework to perform joint material classification task to improve the training effectiveness. The framework is implemented and tested on the two sets of tactile data collected in sliding motion on 20 material textures using the iCub RoboSkin tactile sensors and the SynTouch BioTac sensor respectively. Our results show that the learning efficiency and accuracy improve for both datasets through the joint learning as compared to independent dataset training. This suggests the usefulness for large-scale open tactile datasets sharing with different sensors.