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. "How to do it and with what technology, we haven't decided yet. And that's what we're seeking help for", says Technology Engineer Peter Lund Andersen from Danfoss Drives.
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.
MODEX, ProMat and CeMAT are the biggest global material handling and logistics supply chain tech trade shows. But as I walked the corridors of this year's MODEX in Atlanta, I was particularly aware of the widening disparity between the old and new. The 2018 MHI Annual Industry Report found that the 2018 adoption rate for driverless vehicles in material handling was only 10% and the adoption rate for AI was just 5% while the rate for robotics and automation was 35%. Human-operated AGVs, tows, lifts and other warehouse and factory vehicles have been a staple in material movement for decades. Now, with low-cost cameras, sensors and advanced vision systems, they are slowly transitioning to more flexible autonomous mobile robots that can tow, lift and carry.
With the Robot Launch 2018 competition in full swing – deadline May 15 for entries wanting to compete on stage in Brisbane at ICRA 2018 – we thought it was time to look at last years' Robot Launch finalists. And a very successful bunch they are too! Tennibot won the CES 2018 Innovation Award, was covered in media like Times, Discovery Channel and LA Times. Tennibot also won $40,000 from the Alabama Launchpad competition and are launching a crowdfunding campaign today! Tennibot uses computer vision and artificial intelligence to locate/pick up tennis balls and navigate on the court.
In this episode of Robots in Depth, Per Sjöborg speaks with David Johan, Co-Founder and CEO of Shape Robotics. David started early in robotics, getting involved already in high school. At university he founded the EU project Hydra, that introduced him to modular robotics. In the Hydra project he participated in developing, among other things, the Atron self-reconfiguring modular robotic system. We also hear how the Fable system emerged from co-operating with Lego and how it's used all the way from 4th grade to university research.
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.
During his presentation, Dr. Lupashin of ETH Zurich attached a dog leash to an aerial drone while declaring to the audience, "there has to be another way" of flying robots safely around people. Lupashin's creativity eventually led to the invention of Fotokite and one of the most successful Indiegogo campaigns. Since Lupashin's demo, there are now close to a hundred providers of drones on leashes from innovative startups to aftermarket solutions in order to restrain unmanned flying vehicles. Probably the best known enterprise solution is CyPhy Works which has raised more than $30 million. Last August, during President Trump's visit to his Golf Course in New Jersey, the Department of Homeland Security (DHS) deployed CyPhy's tethered drones to patrol the permitter.
In this episode, Audrow Nash speaks with Michael Laskey, PhD student at UC Berkeley, about a method for robust imitation learning, called DART. Laskey discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines. To learn more, see this post on Robohub from the Berkeley Artificial Intelligence Research (BAIR) Lab. Michael Laskey is a Ph.D. Candidate in EECS at UC Berkeley, advised by Prof. Ken Goldberg in the AUTOLAB (Automation Sciences). Michael's Ph.D. develops new algorithms for Deep Learning of robust robot control policies and examines how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments.
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? A wealth of inspiration can be drawn from computer graphics, where the physics-based simulation of natural movements have been a subject of intense study for decades. The greater emphasis placed on motion quality is often motivated by applications in film, visual effects, and games.
In the constantly changing landscape of today's global digital workspace, AI's presence grows in almost every industry. Retail giants like Amazon and Alibaba are using algorithms written by machine learning software to add value to the customer experience. Machine learning is also prevalent in the new Service Robotics world as robots transition from blind, dumb and caged to mobile and perceptive. Competition is particularly focused between the US and China even though other countries and global corporations have large AI programs as well. The competition is real, fierce and dramatic.