A Biomimetic Tactile Fingerprint Induces Incipient Slip

The 2020 IEEE/RSJ International On-Demand Conference on Intelligent Robots and Systems (IROS 2020), Las Vegas, United States of America (held online due to coronavirus outbreak), 25 October - 24 December 2020We present a modified TacTip biomimetic optical tactile sensor design which demonstrates the ability to induce and detect incipient slip, as confirmed by recording the movement of markers on the sensor’s external surface. Incipient slip is defined as slippage of part, but not all, of the contact surface between the sensor and object. The addition of ridges - which mimic the friction ridges in the human fingertip - in a concentric ring pattern allowed for localised shear deformation to occur on the sensor surface for a significant duration prior to the onset of gross slip. By detecting incipient slip we were able to predict when several differently shaped objects were at risk of falling and prevent them from doing so. Detecting incipient slip is useful because a corrective action can be taken before slippage occurs across the entire contact area thus minimising the risk of objects been dropped.Science Foundation IrelandInsight Research Centre2020-11-26 JG: Broken PDF replace

Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations

There are a wide range of features that tactile contact provides, each with different aspects of information that can be used for object grasping, manipulation, and perception. In this paper inference of some key tactile features, tip displacement, contact location, shear direction and magnitude, is demonstrated by introducing a novel method of transducing a third dimension to the sensor data via Voronoi tessellation. The inferred features are displayed throughout the work in a new visualisation mode derived from the Voronoi tessellation; these visualisations create easier interpretation of data from an optical tactile sensor that measures local shear from displacement of internal pins (the TacTip). The output values of tip displacement and shear magnitude are calibrated to appropriate mechanical units and validate the direction of shear inferred from the sensor. We show that these methods can infer the direction of shear to $\sim$2.3$^{\circ}$ without the need for training a classifier or regressor. The approach demonstrated here will increase the versatility and generality of the sensors and thus allow sensor to be used in more unstructured and unknown environments, as well as improve the use of these tactile sensors in more complex systems such as robot hands.Comment: Presented at ICRA 201

The TacTip Family : Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies

The authors thank Sam Coupland, Gareth Griffiths, and Samuel Forbes for their help with 3D printing and Jason Welsby for his assistance with electronics. N.L. was supported, in part, by a Leverhulme Trust Research Leadership Award on “A biomimetic forebrain for robot touch” (RL-2016-039), and N.L. and M.E.G. were supported, in part, by an EPSRC grant on Tactile Super-resolution Sensing (EP/M02993X/1). L.C. was supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE).Peer reviewedPublisher PD

From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor

Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this paper, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep CNN can give reliable edge perception and thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve. A video demonstrating the approach can be found at https://www.youtube.com/watch?v=QHrGsG9AHtsComment: Accepted in RAL and ICRA 2019. N. Lepora and J. Lloyd contributed equally to this wor