Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations—partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels.

/pdf/cloth-grasp-point-detection-based-on-multiple-view-geometric-1karf8krbt.pdf

Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding

We consider the problem of autonomous robotic laundry folding, and propose a solution to the perception and manipulation challenges inherent to the task. At the core of our approach is a quasi-static cloth model which allows us to neglect the complex dynamics of cloth under significant parts of the state space, allowing us to reason instead in terms of simple geometry. We present an algorithm which, given a 2D cloth polygon and a desired sequence of folds, outputs a motion plan for executing the corresponding manipulations, deemed g-folds, on a minimal number of robot grippers. We define parametrized fold sequences for four clothing categories: towels, pants, short-sleeved shirts, and long-sleeved shirts, each represented as polygons. We then devise a model-based optimization approach for visually inferring the class and pose of a spread-out or folded clothing article from a single image, such that the resulting polygon provides a parse suitable for these folding primitives. We test the manipulation and perception tasks individually, and combine them to implement an autonomous folding system on the Willow Garage PR2. This enables the PR2 to identify a clothing article spread out on a table, execute the computed folding sequence, and visually track its progress over successive folds.

A geometric approach to robotic laundry folding

An introduction to computer vision illumination and fixturing sensors image acquisition and representation fundamentals of digital image processing image analysis the segmentation problem 2-D shape description and recognition 3-D object representations robot programming and robot vision bin picking trends and aspirations.

Machine vision: automated visual inspection and robot vision

We consider the problem of autonomously bringing an article of clothing into a desired configuration using a general-purpose two-armed robot. We propose a hidden Markov model (HMM) for estimating the identity of the article and tracking the article's configuration throughout a specific sequence of manipulations and observations. At the end of this sequence, the article's configuration is known, though not necessarily desired. The estimated identity and configuration of the article are then used to plan a second sequence of manipulations that brings the article into the desired configuration. We propose a relaxation of a strain-limiting finite element model for cloth simulation that can be solved via convex optimization; this serves as the basis of the transition and observation models of the HMM. The observation model uses simple perceptual cues consisting of the height of the article when held by a single gripper and the silhouette of the article when held by two grippers. The model accurately estimates the identity and configuration of clothing articles, enabling our procedure to autonomously bring a variety of articles into desired configurations that are useful for other tasks, such as folding.

/pdf/bringing-clothing-into-desired-configurations-with-limited-1eech1c6qk.pdf

Bringing clothing into desired configurations with limited perception

A robotic system for handling textile and non rigid flat materials

The authors present a robotic system incorporating vision and force/torque sensing for handling flat textile materials and describe the results of experiments to measure the accuracy and reliability of the system for a variety of representative handling tasks for textile materials.

Robot handling of flat textile materials

This paper investigates the properties of the so-called feedforward method, which is a very simple training law suitable for on-chip learning. Its merit is conceptual and implementational simplicity. Its signals do not propagate in both directions and it works for various types of activation function, a feature that makes it particularly effective in the case of unmodeled activation functions. Extensive simulation has shown that this method is usually faster than backpropagation. >

On the properties of the feedforward method: A simple training law for on-chip learning

A very simple and efficient method for artificial neural networks training is proposed. Extensive simulation has established that it works very well as an ANN training law. It is faster than BackPropagation, it is suitable for on-chip learning and it can be implemented in parallel computers very easily.

A Simple Training Law Suitable for On-Chip Learning.

In this paper we present the results of a learning algorithm based on a recently developed algorithm (the so called feedforward algorithm) in conjuction with structural adaptation. Simulation results of training on random associations and on the two-spirals problem are presented for the proposed algorithm and the backpropagation algorithm.

http://ieeexplore.ieee.org/iel4/5797/15487/00713992.pdf

K. Paraschidis

Papers

A robotic system for handling textile and non rigid flat materials

Robot handling of flat textile materials

On the properties of the feedforward method: A simple training law for on-chip learning

A Simple Training Law Suitable for On-Chip Learning.

Structural adaptation based on a simple learning algorithm