ClearPath Saga Robotics ANYbotics
In this interview, Audrow Nash interviews Ryan Gariepy, Lars Grimstad, and Péter Fankhauser.
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Horizon Magazine - January 17, 2019
By Gareth Willmer
It’s part of a field of work that is building machines that can provide real-time help using only limited data as input. Standard machine-learning algorithms often need to process thousands of possibilities before deciding on a solution, which may be impractical in pressurised scenarios where fast adaptation is critical.
University of Queensland University of Sydney National Taiwan University
In this episode, Audrow Nash interviews Pauline Pound, Philippe Morere, and Yujung Liu about the work they presented at the 2018 International Conference on Intelligent Robots and Systems (IROS) in Madrid, Spain.
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Wyss Institute - January 7, 2019
By Leah Burrows
Children born prematurely often develop neuromotor and cognitive developmental disabilities. The best way to reduce the impacts of those disabilities is to catch them early through a series of cognitive and motor tests. But accurately measuring and recording the motor functions of small children is tricky. As any parent will tell you, toddlers tend to dislike wearing bulky devices on their hands and have a predilection for ingesting things they shouldn’t.
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Brad Templeton, Robocars.com - January 7, 2019
In spite of what most are writing, it was a year of much progress.
A number of other summaries of 2018 in robocars have called it a bad year, the year it all went south, even the year the public realized that robocars will never come.
Bitcraze Blue Danube Robotics IDMind
In this interview, Audrow Nash interviews Kristoffer Richardsson, Michael Zillich, and Paulo Alvito.
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Robohub Editors - December 22, 2018
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Wyss Institute - December 21, 2018
By Lindsay Brownell
Jet engines can have up to 25,000 individual parts, making regular maintenance a tedious task that can take over a month per engine. Many components are located deep inside the engine and cannot be inspected without taking the machine apart, adding time and costs to maintenance. This problem is not only confined to jet engines, either; many complicated, expensive machines like construction equipment, generators, and scientific instruments require large investments of time and money to inspect and maintain.
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Sabine Hauert - December 19, 2018
Work by I. Slavkov, D. Carrillo-Zapata, N. Carranza, X. Diego, F. Jansson, J. Kaandorp, S. Hauert, J. Sharpe
Our work published today in Science Robotics describes how we grow fully self-organised shapes using a swarm of 300 coin-sized robots. The work was led by James Sharpe at EMBL and the Centre for Genomic Regulation (CRG) in Barcelona – together with my team at the Bristol Robotics Laboratory and University of Bristol.
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Robohub Editors - December 16, 2018
That’s right! You better not run, you better not hide, you better watch out for brand new robot holiday videos on Robohub! Drop your submissions down our chimney at sabine.hauert@robohub.org and share the spirit of the season.
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QUT - December 16, 2018
In a world first, an undersea robot has dispersed microscopic baby corals (coral larvae) to help scientists working to repopulate parts of the Great Barrier Reef during this year’s mass coral spawning event.
Six weeks after winning the Great Barrier Reef Foundation’s $300,000 Out of the Blue Box Reef Innovation Challenge, Southern Cross University’s Professor Peter Harrison and QUT’s Professor Matthew Dunbabin trialled the ground-breaking initiative on Vlasoff Reef, near Cairns in north Queensland.
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Horizon Magazine - December 16, 2018
by Steve Gillman
Every year 7 million hectares of forest are cut down, chipping away at the 485 gigatonnes of carbon dioxide (CO2) stored in trees around the world, but low-cost drones and new satellite imaging could soon protect these carbon stocks and help developing countries get paid for protecting their trees.
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MIT News - December 16, 2018
Photo courtesy of CSAIL
By Kim Martineau
Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we’re done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable.
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BAIR Blog - December 16, 2018
By Tuomas Haarnoja, Vitchyr Pong, Kristian Hartikainen, Aurick Zhou, Murtaza Dalal, and Sergey Levine
We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google Brain, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. In this post, we will benchmark SAC against state-of-the-art model-free RL algorithms and showcase a spectrum of real-world robot examples, ranging from manipulation to locomotion. We also release our implementation of SAC, which is particularly designed for real-world robotic systems.
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Oliver Mitchell - December 16, 2018
Earlier this month, I crawled into Dr. Wendy Ju‘s autonomous car simulator to explore the future of human-machine interfaces at CornellTech’s Tata Innovation Center. Dr. Ju recently moved to the Roosevelt Island campus from Stanford University. While in California, the roboticist was famous for making videos capturing people’s reactions to self-driving cars using students disguised as “ghost-drivers” in seat costumes. Professor Ju’s work raises serious questions of the metaphysical impact of docility.
