Using tactile sensors and IMUs with an unsupervised feature-learning approach for robotic grasp stability assessment

In a world where industry has a need for ever more complex automated machines, robot grasping is still far from human capabilities. Despite recent innovations in computer vision and grasping planning, it is still a challenge for a robot to pick never-before-seen objects. Researchers are trying to combine vision with tactile sensing to augment the performance of modern intelligent machines. In this thesis, we will present a novel way to improve robotic grasping using tactile sensors and an unsupervised feature-learning algorithm. Using a test bench and sensors at the Control and Robotics (CoRo) laboratory of the ÉTS, we have developed and tested a series of classifiers to predict the outcome of a robotic grasp. Our method improves upon the results of hand-crafted feature learning. We have collected data from 100 different everyday objects, executing 10 grasping attemps per object, for a total of 1000 grasping attemps. The optimal system we developed recognized grasp failures 84.23% of the time

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

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

Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch

© 2016 IEEE. Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online

A NEUROMORPHIC APPROACH TO TACTILE PERCEPTION

Ph.DDOCTOR OF PHILOSOPH

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Fast and robust learning by reinforcement signals: explorations in the insect brain

We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses