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Category: robotics/AI

Most humans can learn how to complete a given task by observing another person perform it just once. Robots that are programmed to learn by imitating humans, however, typically need to be trained on a series of human demonstrations before they can effectively reproduce the desired behavior.

Researchers were recently able to teach robots to execute new tasks by having them observe a single human demonstration, using meta-learning approaches. However, these learning techniques typically require real-world data that can be expensive and difficult to collect.

To overcome this challenge, a team of researchers at Imperial College London has developed a new approach that enables one-shot imitation learning in robots without the need for real-world human demonstrations. Their approach, presented in a paper pre-published on arXiv, uses algorithms known as task-embedded control networks (TecNets), which allow artificial agents to learn how to complete tasks from a single or multiple demonstrations, as well as artificially generated .

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“Demonstrate That Top Financial and Tech Corporations Are Committed to Longevity”

This week two Landmark International Longevity Summits in London attracted the attention of scientists, government officials, major financial corporations, insurance companies, investment banks, and technology companies from around the world. The Landmark AI for Longevity Summit and the First International Longevity Policy and Governance Summit at King’s College London are expected to become the world-leading forums for the Longevity Industry.

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NASA has recruited SpaceX’s Starship, Blue Origin’s Blue Moon and three other commercial lunar lander ideas to join its Artemis moon program.

Today (Nov. 18), NASA announced the selection of SpaceX, Blue Origin, Sierra Nevada Corp., Ceres Robotics and Tyvak Nano-Satellite Systems, Inc. to join its Commercial Lunar Payload Services program (CLPS). The five companies can now vie to deliver robotic payloads to the lunar surface for NASA, helping to pave the way for the return of astronauts to the moon by 2024.

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China has completed its first public test of a Mars lander, keeping the country on track for an unmanned exploration mission to the red planet in 2020.

The trial took place Thursday in the mountainous landscape of Hebei province, about an hour and a half drive north of Beijing. Chinese officials said the landscape mirrored the slopes and craters of the planet Mars.

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SpaceX is going to launch a payload for client Nanoracks aboard one of its new rideshare missions, currently targeting late 2020, that will demonstrate a very ambitious piece of tech from the commercial space station company. Nanoracks is sending up a payload platform that will show off how it can use a robot to cut material very similar to the upper stages used in orbital spacecraft — something Nanoracks wants to eventually due to help convert these spent and discarded stages (sometimes called “space tugs” because they generally move payloads from one area of orbit to another) into orbital research stations, habitats and more.

The demonstration mission is part of Nanoracks’ “Space Outpost Program,” which aims to address the future need for in-space orbital commercial platforms by also simultaneously making use of existing vehicles and materials designed specifically for space. Through use of the upper stages of spacecraft left behind in orbit, the company hopes to show how it one day might be able to greatly reduce the costs of setting up in-space stations and habitats, broadening the potential access of these kinds of facilities for commercial space companies.

This will be the first-ever demonstration of structural metal cutting in space, provided the demo goes as planned, and it could be a key technology not just for establishing more permanent research families in Earth’s orbit, but also for setting up infrastructure to help us get to, and stay at, other interstellar destinations like the Moon and Mars.

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Artificial intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method developed by a team of researchers at the University of Warwick, the Technical University of Berlin and the University of Luxembourg, could be used to speed-up the design of drug molecules or new materials.

Artificial intelligence and are routinely used to predict our purchasing behavior and to recognize our faces or handwriting. In , Artificial Intelligence is establishing itself as a crucial tool for scientific discovery.

In chemistry, AI has become instrumental in predicting the outcomes of experiments or simulations of quantum systems. To achieve this, AI needs to be able to systematically incorporate the fundamental laws of .

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In part two of this 2-part series, Oriana Beaudet and Dan Pesut discuss a healthcare future that includes automation, artificial intelligence and robots. And what about potential disruptive futures that change everything?

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Artificial muscles will power the soft robots and wearable devices of the future. But more needs to be understood about the underlying mechanics of these powerful structures in order to design and build new devices.

Now, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have uncovered some of the fundamental physical properties of artificial muscle fibers.

“Thin soft filaments that can easily stretch, bend, twist or shear are capable of extreme deformations that lead to knot-like, braid-like or loop-like structures that can store or release energy easily,” said L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, of Organismic and Evolutionary Biology, and of Physics. “This has been exploited by a number of experimental groups recently to create prototypical artificial muscle fibers. But how the topology, geometry and mechanics of these slender fibers come together during this process was not completely clear. Our study explains the theoretical principles underlying these shape transformations, and sheds light on the underlying design principles.”

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