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Title: Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision?
Award ID(s):
Publication Date:
Journal Name:
Advanced Robotics
Page Range or eLocation-ID:
661 to 673
Sponsoring Org:
National Science Foundation
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  1. Tactile sensing for robotics is achieved through a variety of mechanisms, including magnetic, optical-tactile, and conductive fluid. Currently, the fluid-based sensors have struck the right balance of anthropomorphic sizes and shapes and accuracy of tactile response measurement. However, this design is plagued by a low Signal to Noise Ratio (SNR) due to the fluid based sensing mechanism “damping” the measurement values that are hard to model. To this end, we present a spatio-temporal gradient representation on the data obtained from fluid-based tactile sensors, which is inspired from neuromorphic principles of event based sensing. We present a novel algorithm (GradTac) that converts discrete data points from spatial tactile sensors into spatio-temporal surfaces and tracks tactile contours across these surfaces. Processing the tactile data using the proposed spatio-temporal domain is robust, makes it less susceptible to the inherent noise from the fluid based sensors, and allows accurate tracking of regions of touch as compared to using the raw data. We successfully evaluate and demonstrate the efficacy of GradTac on many real-world experiments performed using the Shadow Dexterous Hand, equipped with the BioTac SP sensors. Specifically, we use it for tracking tactile input across the sensor’s surface, measuring relative forces, detecting linear andmore »rotational slip, and for edge tracking. We also release an accompanying task-agnostic dataset for the BioTac SP, which we hope will provide a resource to compare and quantify various novel approaches, and motivate further research.« less
  2. This paper introduces a vision-based tactile sensor FingerVision, and explores its usefulness in tactile behaviors. FingerVision consists of a transparent elastic skin marked with dots, and a camera that is easy to fabricate, low cost, and physically robust. Unlike other vision-based tactile sensors, the complete transparency of the FingerVision skin provides multimodal sensation. The modalities sensed by FingerVision include distributions of force and slip, and object information such as distance, location, pose, size, shape, and texture. The slip detection is very sensitive since it is obtained by computer vision directly applied to the output from the FingerVision camera. It provides high-resolution slip detection, which does not depend on the contact force, i.e., it can sense slip of a lightweight object that generates negligible contact force. The tactile behaviors explored in this paper include manipulations that utilize this feature. For example, we demonstrate that grasp adaptation with FingerVision can grasp origami, and other deformable and fragile objects such as vegetables, fruits, and raw eggs.