Neuromorphic Computing Systems for Tactile Sensing Perception

Touch sensing plays an important role in humans daily life. Tasks like exploring, grasping and manipulating objects deeply rely on it. As such, Robots and hand prosthesis endowed with the sense of touch can better and more easily manipulate objects, and physically collaborate with other agents. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. The orientation of edges, which is employed as a pre-processing stage in both artificial vision and touch, is a key indication for object discrimination. Inspired on the encoding of edges in human first-order tactile afferents, we developed a biologically inspired, spiking models architecture that mimics human tactile perception with computational primitives that are implementable on low-power subthreshold neuromorphic hardware. The network architecture uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. We demonstrated that the network architecture can learn the appropriate connectivity through unsupervised spike-based learning, and that the number and spatial distribution of sensitive areas within receptive fields are important in edge orientation discrimination. The unconstrained and random structure of the connectivity among layers can produce unbalanced activity in the output neurons, which are driven by a variable amount of synaptic inputs. We explored two different mechanisms of synaptic normalization (weights normalization and homeostasis), defining how this can be useful during the learning phase and inference phase. The network is successfully able to discriminate between 35 orientations of 36 (0 degree to 180 degree with 5 degree step increments) with homeostasis and weights normalization mechanism. Besides edge orientation discrimination, we modified the network architecture to be able to classify six different touch modalities (e.g. poke, press, grab, squeeze, push, and rolling a wheel). We demonstrated the ability of the network to learn appropriate connectivity patterns for the classification, achieving a total accuracy of 88.3 %. Furthermore, another application scenario on the tactile object shapes recognition has been considered because of its importance in robotic manipulation. We illustrated that the network architecture with 2 layers of spiking neurons was able to discriminate the tactile object shapes with accuracy 100 %, after integrating to it an array of 160 piezoresistive tactile sensors where the object shapes are applied

Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor

Manipulation tasks often require robots to be continuously in contact with an object. Therefore tactile perception systems need to handle continuous contact data. Shear deformation causes the tactile sensor to output path-dependent readings in contrast to discrete contact readings. As such, in some continuous-contact tasks, sliding can be regarded as a disturbance over the sensor signal. Here we present a shear-invariant perception method based on principal component analysis (PCA) which outputs the required information about the environment despite sliding motion. A compliant tactile sensor (the TacTip) is used to investigate continuous tactile contact. First, we evaluate the method offline using test data collected whilst the sensor slides over an edge. Then, the method is used within a contour-following task applied to 6 objects with varying curvatures; all contours are successfully traced. The method demonstrates generalisation capabilities and could underlie a more sophisticated controller for challenging manipulation or exploration tasks in unstructured environments. A video showing the work described in the paper can be found at https://youtu.be/wrTM61-pieUComment: Accepted in ICRA 201

Artificial Bio-inspired Tactile Receptive Fields for Edge Orientation Classification

Robots and users of hand prosthesis could easily manipulate objects if endowed with the sense of touch. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. An important cue for objects discrimination is the orientation of edges, that is used both in artificial vision and touch as pre-processing stage. We present a spiking neural network, inspired on the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations (from 0o to 180o), by implementing a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike based learning, and that the number and spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination

Soft Morphological Processing of Tactile Stimuli for Autonomous Category Formation

Sensor morphology is a fundamental aspect of tactile sensing technology. Design choices induce stimuli to be morphologically processed, changing the sensory perception of the touched objects and affecting inference at a later processing stage. We develop a framework to analyze the filtered sensor response and observe the correspondent change in tactile information. We test the morphological processing effects on the tactile stimuli by integrating a capacitive tactile sensor into a flat end-effector and creating three soft silicon-based filters with varying thickness (3mm, 6mm and 10mm). We incorporate the end-effector onto a robotic arm. We control the arm in order to apply a calibrated force onto 4 objects, and retrieve tactile images. We create an unsupervised inference process through the use of Principal Component Analysis and K-Means Clustering.We use the process to group the sensed objects into 2 classes and observe how different soft filters affect the clustering results. The sensor response with the 3mm soft filter allows for edges to be the feature with most variance (captured by PCA) and induces the association of edged objects. With thicker soft filters the associations change, and with a 10mm filter the sensor response results more diverse for objects with different elongation. We show that the clustering is intrinsically driven by the morphology of the sensor and that the robot’s world understanding changes according to it.This work was funded by the UK Agriculture and Horticulture Development Board and by The United Kingdom Engineering and Physical Sciences Research Council (EPSRC) MOTION grant [EP/N03211X/2]

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page