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Soft robotics actuators heal themselves

Robohub

Repeated activity wears on soft robotic actuators, but these machines' moving parts need to be reliable and easily fixed. Now a team of researchers has a biosynthetic polymer, patterned after squid ring teeth, that is self-healing and biodegradable, creating a material not only good for actuators, but also for hazmat suits and other applications where tiny holes could cause a danger. "Current self-healing materials have shortcomings that limit their practical application, such as low healing strength and long healing times (hours)," the researchers report in today's (July 27) issue of Nature Materials. The researchers produced high-strength synthetic proteins that mimic those found in nature. Like the creatures they are patterned on, the proteins can self-heal both minute and visible damage.


Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage

arXiv.org Machine Learning

This paper has been accepted by IEEE Transactions on Automation Science and Engineering. 1 This preprint is an accepted version, not the IEEE published version. Abstract--In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for the industrial applications where training samples are expensive, time-consuming, or difficult to obtain. Existing methods mainly focus on active learning for classification, and a few methods are designed for regression such as linear regression or Gaussian process. Uncertainties from measurement errors and intrinsic input noise inevitably exist in the experimental data, which further affects the modeling performance. The existing active learning methods do not incorporate these uncertainties for Gaussian process. In this paper, we propose two new active learning algorithms for the Gaussian process with uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. Through numerical study, we show that the proposed approach can incorporate the impact from uncertainties, and realize better prediction performance. This approach has been applied to improving the predictive modeling for automatic shape control of composite fuselage. I. INTRODUCTION Active learning is a type of iterative supervised learning which focuses on maximizing information acquisition with limited samples. In statistics literature, this process is also called optimal experimental design, or sequential design. The main idea of active learning is to iteratively pose "query" or "design" to explore the most informative new experimental samples according to the information obtained from the current samples. In many machine learning applications, especially in some industrial systems, the explanatory data are rich and easy to get, but the response data are very expensive, time-consuming, or difficult to obtain. For example, when training autonomous driving algorithms, a lot of media (e.g., images, videos) require that oracle users mark them with particular labels, such as "vehicle", "street sign" or "road lines". It can be tedious, redundant and time-consuming to annotate lots of these instances.


Everything You Ever Wanted To Know About Robots

#artificialintelligence

Modern robots are not unlike toddlers: It's hilarious to watch them fall over, but deep down we know that if we laugh too hard, they might develop a complex and grow up to start World War III. None of humanity's creations inspires such a confusing mix of awe, admiration, and fear: We want robots to make our lives easier and safer, yet we can't quite bring ourselves to trust them. We're crafting them in our own image, yet we are terrified they'll supplant us. But that trepidation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even leap among us.


The WIRED Guide to Robots - NewsLagoon

#artificialintelligence

Modern robots are not unlike toddlers: It's hilarious to watch them fall over, but deep down we know that if we laugh too hard, they might develop a complex and grow up to start World War III. None of humanity's creations inspires such a confusing mix of awe, admiration, and fear: We want robots to make our lives easier and safer, yet we can't quite bring ourselves to trust them. We're crafting them in our own image, yet we are terrified they'll supplant us. But that trepidation is no obstacle to the booming field of robotics. Robots have finally grown smart enough and physically capable enough to make their way out of factories and labs to walk and roll and even leap among us.


The Tentacle Bot

Robohub

Of all the cool things about octopuses (and there are a lot), their arms may rank among the coolest. Two-thirds of an octopus's neurons are in its arms, meaning each arm literally has a mind of its own. The hundreds of suckers that cover their arms can form strong seals even on rough surfaces underwater. Imagine if a robot could do all that. Researchers at Harvard's Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences (SEAS) and colleagues from Beihang University have developed an octopus-inspired soft robotic arm that can grip, move, and manipulate a wide range of objects.


Scientists create robots that 'sweat' like humans during demanding tasks to stop them overheating

Daily Mail - Science & tech

Robots have been created by scientists that'sweat' like humans during demanding tasks to stop them overheating. Robotic technology is advancing every day and machines are being given more demanding tasks that generate more heat as a by product. This heat could cause the robot to malfunction if it doesn't cool down, which prompted researchers from Cornell University to look at how humans get cool. They developed a technique that allows machines to'sweat' off cooling liquid stored around the component responsible for moving and controlling the system. They developed a technique that allows machines to'sweat' off cooling liquid stored around the component responsible for moving and controlling the system It is still an early prototype with a number of problems including the sweating process causing the robot to struggle to move.


The case for ... cities where you're the sensor, not the thing being sensed

The Guardian

"Smart city" is one of those science fiction phrases seemingly designed to make you uneasy, like "neuromarketing" or "pre-crime". It's impossible to be alive in this decade and not find something unsettling in the idea of our cities becoming "smart". It's not hard to see why: "smart" has become code for "terrible". A "smart speaker" is a speaker that eavesdrops on you and leaks all your conversations to distant subcontractors for giant tech companies. "Smart watches" spy on your movements and sell them to data-brokers for ad-targeting.


Universal Hysteresis Identification Using Extended Preisach Neural Network

arXiv.org Machine Learning

Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore, several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired by the Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model. It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop neurons, which their activation function follows Deteriorating Stop operator. Deteriorating Stop operators can generate non-congruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps the neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, besides input data the rate at which input data changes, is included as well in order to give the model the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has the capability of the simulation of both rate-independent and rate-dependent hysteresis with either congruent or non-congruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training the model which is based on a combination of the Genetic Algorithm and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it to various hysteresis from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses.


Pieces of plastic have been trained to WALK when triggered by light

Daily Mail - Science & tech

Plastic has been taught to walk when a light shines on it in an experiment that could lead to the creation of artificial muscles, its developers claim. The pieces of plastic are made from thermo-responsive liquid crystal polymer and a coat of dye. They can convert energy into mechanical motion - simulating a walk. Researchers from Aalto University in Finland say the plastic is a programmable soft-robot that could be used in bio-medicine. Their biggest breakthrough was being able to teach it to respond to light sources rather than having to use heat to warp the plastic to generate movement.


Internet of Things Solutions for Automotive Industry

#artificialintelligence

As the world embraces advances in technology, all the aspects of our lives are being connected. The modern vehicle is no exception here. Automotive manufacturers continuously increase the use of electronics systems to improve vehicle performance, safety, and passenger comfort. The integration of sensors and actuators with automotive components is no more surprise today. Such integration offers optimized vehicle performance, improved reliability, and enhanced durability.