Tactile on-chip pre-processing with techniques from artificial retinas

The interest in tactile sensors is increasing as their use in complex unstructured environments is demanded, like in tele-presence, minimal invasive surgery, robotics etc. The matrix of pressure data these devices provide can be managed with many image processing algorithms to extract the required information. However, as in the case of vision chips or artificial retinas, problems arise when the array size and the computation complexity increase. Having a look to the skin, the information collected by every mechanoreceptor is not carried to the brain for its processing, but some complex pre-processing is performed to fit the limited throughput of the nervous system. This is specially important for high bandwidth demanding tasks. Experimental works report that neural response of skin mechanoreceptors encodes the change in local shape from an offset level rather than the absolute force or pressure distributions. This is also the behavior of the retina, which implements a spatio-temporal averaging. We propose the same strategy in tactile preprocessing, and we show preliminary results when it faces the detection of the slip, which involves fast real-time processing.Ministerio de Ciencia y Tecnología TIC2003 - 09817-C0

Development of an intelligent object for grasp and manipulation research

Kõiva R, Haschke R, Ritter H. Development of an intelligent object for grasp and manipulation research. Presented at the ICAR 2011, Tallinn, Estonia.In this paper we introduce a novel device, called iObject, which is equipped with tactile and motion tracking sensors that allow for the evaluation of human and robot grasping and manipulation actions. Contact location and contact force, object acceleration in space (6D) and orientation relative to the earth (3D magnetometer) are measured and transmitted wirelessly over a Bluetooth connection. By allowing human-human, human-robot and robot-robot comparisons to be made, iObject is a versatile tool for studying manual interaction. To demonstrate the efficiency and flexibility of iObject for the study of bimanual interactions, we report on a physiological experiment and evaluate the main parameters of the considered dual-handed manipulation task

Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors

Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings -- part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direction of the slip motion. Further, we test our detector on two robot manipulation tasks involving common household objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for complex manipulation tasks such as slip compensation.Comment: Submitted to th RoboTac Workshop in the IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS'21), Prague, Czech Republic, Sept 27- Oct 1, 202

Dexterous manipulation of unknown objects using virtual contact points

The manipulation of unknown objects is a problem of special interest in robotics since it is not always possible to have exact models of the objects with which the robot interacts. This paper presents a simple strategy to manipulate unknown objects using a robotic hand equipped with tactile sensors. The hand configurations that allow the rotation of an unknown object are computed using only tactile and kinematic information, obtained during the manipulation process and reasoning about the desired and real positions of the fingertips during the manipulation. This is done taking into account that the desired positions of the fingertips are not physically reachable since they are located in the interior of the manipulated object and therefore they are virtual positions with associated virtual contact points. The proposed approach was satisfactorily validated using three fingers of an anthropomorphic robotic hand (Allegro Hand), with the original fingertips replaced by tactile sensors (WTS-FT). In the experimental validation, several everyday objects with different shapes were successfully manipulated, rotating them without the need of knowing their shape or any other physical property.Peer ReviewedPostprint (author's final draft

Tactile sensors for robot handling

First and second generation robots have been used cost effectively in high‐volume ‘fixed’ or ‘hard’ automated manufacturing/assembly systems. They are ‘limited‐ability’ devices using simple logic elements or primitive sensory feedback. However, in the unstructured environment of most manufacturing plants it is often necessary to locate, identify, orientate and position randomly presented components. Visual systems have been researched and developed to provide a coarse resolution outline of objects. More detailed and precise definition of parts is usually obtained by high resolution tactile sensing arrays. This paper reviews and discusses the current state of the art in tactile sensing

Active Clothing Material Perception using Tactile Sensing and Deep Learning

Humans represent and discriminate the objects in the same category using their properties, and an intelligent robot should be able to do the same. In this paper, we build a robot system that can autonomously perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. Those properties include the physical properties, like thickness, fuzziness, softness and durability, and semantic properties, like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616 robot exploring iterations on them. To extract the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can autonomously explore the unknown clothes and learn their properties. This work proposes a new framework for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.Comment: ICRA 2018 accepte