Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching

TL;DR

This paper introduces a novel multimodal recognition system that uses tactile and visual data to identify objects, demonstrating high accuracy even with unseen objects and outperforming human volunteers.

Contribution

It is the first large-scale study addressing touch-based instance recognition using multimodal data, with a self-supervised data collection approach and a model that surpasses existing methods.

Findings

Accurately recognizes objects by touch alone.

Outperforms human volunteers in object recognition.

Effective on novel, unseen objects.

Abstract

Much of the literature on robotic perception focuses on the visual modality. Vision provides a global observation of a scene, making it broadly useful. However, in the domain of robotic manipulation, vision alone can sometimes prove inadequate: in the presence of occlusions or poor lighting, visual object identification might be difficult. The sense of touch can provide robots with an alternative mechanism for recognizing objects. In this paper, we study the problem of touch-based instance recognition. We propose a novel framing of the problem as multi-modal recognition: the goal of our system is to recognize, given a visual and tactile observation, whether or not these observations correspond to the same object. To our knowledge, our work is the first to address this type of multi-modal instance recognition problem on such a large-scale with our analysis spanning 98 different objects. We employ a robot equipped with two GelSight touch sensors, one on each finger, and a self-supervised, autonomous data collection procedure to collect a dataset of tactile observations and images. Our experimental results show that it is possible to accurately recognize object instances by touch alone, including instances of novel objects that were never seen during training. Our learned model outperforms other methods on this complex task, including that of human volunteers.