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by   -   December 19, 2018

Artistic photo taken by Jerry H. Wright showing a hand-made shape generated following an emergent Turing pattern (displayed using the LEDs). The trajectory of one of the moving robots can be seen through long exposure. Jerry also used a filter to see the infrared communication between the robots (white light below the robots reflected on the table). Reprinted with permission from AAAS.

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.

by   -   December 16, 2018

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   -   December 16, 2018

Better tracking of forest data will make the climate change reporting process easier for countries who want compensation for protecting their carbon stock. Image credit – lubasi, licensed under CC BY-SA 2.0

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.

by   -   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.

by   -   December 4, 2018


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.

by   -   November 14, 2018

By Esther Rolf∗, David Fridovich-Keil∗, and Max Simchowitz

In many tasks in machine learning, it is common to want to answer questions given fixed, pre-collected datasets. In some applications, however, we are not given data a priori; instead, we must collect the data we require to answer the questions of interest.

by   -   November 4, 2018
ANYmal

A crucial task for energy providers is the reliable and safe operation of their plants, especially when producing energy offshore. Autonomous mobile robots are able to offer comprehensive support through regular and automated inspection of machinery and infrastructure. In a world’s first pilot installation, transmission system operator TenneT tested the autonomous legged robot ANYmal on one of the world’s largest offshore converter platforms in the North Sea.

by   -   November 4, 2018


Researchers from EPFL and Stanford have developed small drones that can land and then move objects that are 40 times their weight, with the help of powerful winches, gecko adhesives and microspines.

by   -   October 24, 2018

By Daniel Seita, Jeff Mahler, Mike Danielczuk, Matthew Matl, and Ken Goldberg

This post explores two independent innovations and the potential for combining them in robotics. Two years before the AlexNet results on ImageNet were released in 2012, Microsoft rolled out the Kinect for the X-Box. This class of low-cost depth sensors emerged just as Deep Learning boosted Artificial Intelligence by accelerating performance of hyper-parametric function approximators leading to surprising advances in image classification, speech recognition, and language translation.

by   -   October 19, 2018

Researchers are using computer simulations to estimate how 11 different species of extinct archosaurs such as the batrachotomus might have moved. Image credit: John Hutchinson

By Sandrine Ceurstemont

From about 245 to 66 million years ago, dinosaurs roamed the Earth. Although well-preserved skeletons give us a good idea of what they looked like, the way their limbs worked remains a bigger mystery. But computer simulations may soon provide a realistic glimpse into how some species moved and inform work in fields such as robotics, prosthetics and architecture.

by   -   October 18, 2018

By Xue Bin (Jason) Peng and Angjoo Kanazawa

Whether it’s everyday tasks like washing our hands or stunning feats of acrobatic prowess, humans are able to learn an incredible array of skills by watching other humans. With the proliferation of publicly available video data from sources like YouTube, it is now easier than ever to find video clips of whatever skills we are interested in.

by , and   -   October 6, 2018

The deployment of connected, automated, and autonomous vehicles presents us with transformational opportunities for road transport. These opportunities reach beyond single-vehicle automation: by enabling groups of vehicles to jointly agree on maneuvers and navigation strategies, real-time coordination promises to improve overall traffic throughput, road capacity, and passenger safety. However, coordinated driving for intelligent vehicles still remains a challenging research problem, and testing new approaches is cumbersome. Developing true-scale facilities for safe, controlled vehicle testbeds is massively expensive and requires a vast amount of space. One approach to facilitating experimental research and education is to build low-cost testbeds that incorporate fleets of down-sized, car-like mobile platforms.

by   -   September 18, 2018

The multi-joint soft exosuit consists of textile apparel components worn at the waist, thighs and calves that guide mechanical forces from an optimized mobile actuation system attached to a rucksack via cables to the ankle and hip joints. In addition, a new tuning method helps personalize the exosuit’s effects to wearers’ specific gaits. Credit: Harvard Biodesign Lab

By Benjamin Boettner

In the future, smart textile-based soft robotic exosuits could be worn by soldiers, fire fighters and rescue workers to help them traverse difficult terrain and arrive fresh at their destinations so that they can perform their respective tasks more effectively. They could also become a powerful means to enhance mobility and quality of living for people suffering from neurodegenerative disorders and for the elderly.

by   -   September 18, 2018

Open-Source Software for robots is a de-facto standard in academia, and its advantages can benefit industrial applications as well. The worldwide ROS-Industrial initiative has been using ROS, the Robot Operating System, to this end.

by   -   September 10, 2018

Applications from 166 companies spread across 12 European countries and myriads of exiting robotics ideas was the beginning of the EU-funded initiative ROBOTT-NET in 2016.

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On Artificial Intelligence for Wildlife Conservation
June 11, 2019


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