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!
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.
By Catherine Collins
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.
By Leah Burrows
Even octopuses understand the importance of elbows. When these squishy, loose-limbed cephalopods need to make a precise movement — such as guiding food into their mouth — the muscles in their tentacles contract to create a temporary revolute joint. These joints limit the wobbliness of the arm, enabling more controlled movements.
By Siddharth Reddy
Imagine a drone pilot remotely flying a quadrotor, using an onboard camera to navigate and land. Unfamiliar flight dynamics, terrain, and network latency can make this system challenging for a human to control. One approach to this problem is to train an autonomous agent to perform tasks like patrolling and mapping without human intervention. This strategy works well when the task is clearly specified and the agent can observe all the information it needs to succeed. Unfortunately, many real-world applications that involve human users do not satisfy these conditions: the user’s intent is often private information that the agent cannot directly access, and the task may be too complicated for the user to precisely define. For example, the pilot may want to track a set of moving objects (e.g., a herd of animals) and change object priorities on the fly (e.g., focus on individuals who unexpectedly appear injured). Shared autonomy addresses this problem by combining user input with automated assistance; in other words, augmenting human control instead of replacing it.
Short delivery time, high flexibility and reduced costs for handling parts before assembly. These are the main goals that Danfoss Drives wanted to achieve by creating an automated assembly line. But while the goals were clear, the way to achieve them was cloudier.
The choice of gait, that is whether we walk or run, comes to us so naturally that we hardly ever think about it. We walk at slow speeds and run at high speeds. If we get on a treadmill and slowly crank up the speed, we will start out with walking, but at some point we will switch to running; involuntarily and simply because it feels right. We are so accustomed to this, that we find it rather amusing to see someone walking at high speeds, for example, during the racewalk at the Olympics. This automatic choice of gait happens in almost all animals, though sometimes with different gaits. Horses, for example, tend to walk at slow speeds, trot at intermediate speeds, and gallop at high speeds. What is it that makes walking better suited for low speeds and running better for high speeds? How do we know that we have to switch, and why don’t we skip or gallop like horses? What exactly is it that constitutes walking, running, trotting, galloping, and all the other gaits that can be found in nature?
Motion control problems have become standard benchmarks for reinforcement learning, and deep RL methods have been shown to be effective for a diverse suite of tasks ranging from manipulation to locomotion. However, characters trained with deep RL often exhibit unnatural behaviours, bearing artifacts such as jittering, asymmetric gaits, and excessive movement of limbs. Can we train our characters to produce more natural behaviours?
It all started with 166 companies spread across 12 European countries appling for a “golden ticket” to ROBOTT-NET’s Voucher Program. 64 companies received a voucher and highly specialized consultancy from a broad range of the brightest robotics experts around Europe. Now five of the 64 projects have been selected for a ROBOTT-NET pilot.
The European project ROBOTT-NET helps the best ideas in industrial robotics become reality. 400 hours of free consulting with robotics experts from all over Europe has helped companies, both small and large, find out how robot automation can contribute to them and which automation solution is the right one for each company.
July 8, 2018
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