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 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.

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.

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.

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.

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.

In this post, we demonstrate how deep reinforcement learning (deep RL) can be used to learn how to control dexterous hands for a variety of manipulation tasks. We discuss how such methods can learn to make use of low-cost hardware, can be implemented efficiently, and how they can be complemented with techniques such as demonstrations and simulation to accelerate learning.

By John Miller

An earlier version of this post was published on Off the Convex Path. It is reposted here with the author’s permission.

In the last few years, deep learning practitioners have proposed a litany of different sequence models. Although recurrent neural networks were once the tool of choice, now models like the autoregressive Wavenet or the Transformer are replacing RNNs on a diverse set of tasks. In this post, we explore the trade-offs between recurrent and feed-forward models.

Three projects have made the final cut and received funding for a ROBOTT-NET pilot and even further development assistance.

Since programming is an extremely time-consuming business, small and medium-sized enterprises (SME) are often forced to manage without robots. Researchers from Fraunhofer IPA have therefore developed the software RobotKit specially for welding tasks. In an initial test scenario, the kit reduced programming times from 90 down to just 7 minutes.

By Tianhe Yu and Chelsea Finn

Learning a new skill by observing another individual, the ability to imitate, is a key part of intelligence in human and animals. Can we enable a robot to do the same, learning to manipulate a new object by simply watching a human manipulating the object just as in the video below?

By Fisher Yu

TL;DR, we released the largest and most diverse driving video dataset with richannotations called BDD100K. You can access the data for research now at http://bdd-data.berkeley.edu. We haverecently released an arXivreport on it. And there is still time to participate in our CVPR 2018 challenges!

By Vitchyr Pong
You’ve decided that you want to bike from your house by UC Berkeley to the Golden Gate Bridge. It’s a nice 20 mile ride, but there’s a problem: you’ve never ridden a bike before!

ANYbotics led the way in the ICRA 2018 Robot Launch Startup Competition on May 22, 2018 at the Brisbane Conference Center in Australia. Although ANYbotics pitched last out of the 10 startups presenting, they clearly won over the judges and audience. As competition winners, ANYbotics received a $3,000 prize from QUT bluebox, Australia’s robotics accelerator (currently taking applications for 2018!), plus Silicon Valley Robotics membership and mentoring from The Robotics Hub.

The cost of sea litter in the EU has been estimated at up to €630 million per year. It is mostly composed of plastics, which take hundreds of years to break down in nature, and has the potential to affect human health through the food chain because plastic waste is eaten by the fish that we consume.