Deep Learning for Robotics: The latest news & trends
Advancements in reinforcement learning, computer vision, human-robot interaction, plus robot manufacturer innovations, will create the next generation of skilled robotics impacting our world. How can deep learning help write pioneering robot software and enable new mastery of skills and behaviour?
On June 28-29, RE•WORK is bringing the Deep Learning for Robotics Summit to San Francisco to bring together influential researchers, disruptive startups, and leading robotic companies to explore how we can improve robotic skills via applied deep learning. We’re taking a look at the latest news and trends in the world of deep learning for robotics to give you a taster of what we will be covering at the summit:
By 2020 industry analysts believe the cobots market could surge to $3 billion
‘Created specifically to work alongside humans rather than replacing them, sales of collaborative robots—cobots for short—are surging.
In 2016, cobots accounted for less than five percent of global industrial robot sales; but by 2020 industry analysts believe the cobots market could surge to $3 billion, with 150,000 units in use—a number they say could more than quadruple by 2025.
If current trends continue, 2018 will be remembered as the year that cobots really entered the mainstream.
There is a growing realization that however advanced, robots cannot emulate the human touch; but they can work in tandem with humans to turbocharge productivity. The cobot revolution is not expected to be limited to manufacturing; industries such as retail, healthcare, hospitality and food service also are expected to deploy legions of cobots.’
Deep learning with synthetic data will democratize the tech industry
‘Synthetic data is computer-generated data that mimics real data; in other words, data that is created by a computer, not a human. Software algorithms can be designed to create realistic simulated, or “synthetic,” data.
This synthetic data then assists in teaching a computer how to react to certain situations or criteria, replacing real-world-captured training data. One of the most important aspects of real or synthetic data is to have accurate labels so computers can translate visual data to have meaning. Robotics is another sector leveraging synthetic data to train robots for various activities in factories, warehouses and across society.
Josh Tobin is a research scientist at OpenAI, a nonprofit artificial intelligence research company that aims to promote and develop friendly AI in such a way as to benefit humanity as a whole. Tobin is part of a team working on building robots that learn. They have trained entirely with simulated data and deployed on a physical robot, which, amazingly, can now learn a new task after seeing an action done once.
They developed and deployed a new algorithm called one-shot imitation learning, allowing a human to communicate how to do a new task by performing it in virtual reality. Given a single demonstration, the robot is able to solve the same task from an arbitrary starting point and then continue the task.’
Japan Wants Robots to Help Build Its Skyscrapers
‘It takes about half a million man-days to erect a 30-story office tower, a number that hasn’t changed much over the years, because building sites have remained largely impervious to advances in automation. That’s becoming a problem for construction companies in Japan.
The industry, which bore the brunt of Japan’s prolonged labor shortage, is expecting the situation to get worse with at least a million people set to leave or retire from the profession over the next decade. Shimizu Corp., a Japanese general contractor founded more than 200 years ago, is seeking to soften the blow by introducing robots that can weld beams, haul supplies and install ceiling panels.’
Carnegie Mellon welcomes our robot overlords with first-ever AI undergraduate degree
‘Carnegie Mellon’s School of Computer Science will offer an undergraduate degree in artificial intelligence starting in the upcoming fall semester. The Pittsburgh-based school is the first to offer such a program in the US. Many industry experts believe there aren’t enough qualified candidates in the workforce to fill all the vacancies that technology companies have for people with AI-related skills. This program could contribute quality candidates, though perhaps, more importantly, the prestige of Carnegie Mellon may spur other institutions to offer similar programs.’