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.
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.
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.
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 firstname.lastname@example.org and share the spirit of the season.
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.
A research team from the University of Zurich and EPFL has developed a new drone that can retract its propeller arms in flight and make itself small to fit through narrow gaps and holes. This is particularly useful when searching for victims of natural disasters.
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.
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.
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.
Elowan is a cybernetic lifeform, a plant in direct dialogue with a machine. Using its own internal electrical signals, the plant is interfaced with a robotic extension that drives it toward light.
Plants are electrically active systems. They get bio-electrochemically excited and conduct these signals between tissues and organs. Such electrical signals are produced in response to changes in light, gravity, mechanical stimulation, temperature, wounding, and other environmental conditions.
“The Laughing Room,” an interactive art installation by author, illustrator, and MIT graduate student Jonathan “Jonny” Sun, looks like a typical living room: couches, armchairs, coffee table, soft lighting. This cozy scene, however, sits in a glass-enclosed space, flanked by bright lights and a microphone, with a bank of laptops and a video camera positioned across the room. People wander in, take a seat, begin chatting. After a pause in the conversation, a riot of canned laughter rings out, prompting genuine giggles from the group.
By Chelsea Finn∗, Frederik Ebert∗, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine
With very little explicit supervision and feedback, humans are able to learn a wide range of motor skills by simply interacting with and observing the world through their senses. While there has been significant progress towards building machines that can learn complex skills and learn based on raw sensory information such as image pixels, acquiring large and diverse repertoires of general skills remains an open challenge. Our goal is to build a generalist: a robot that can perform many different tasks, like arranging objects, picking up toys, and folding towels, and can do so with many different objects in the real world without re-learning for each object or task.