After starting off 2018 strong, NVIDIA continued its momentum and finished off the year celebrating the opening of its new AI Robotics Research Lab in Seattle. The lab, which opened November 14, is led by Dieter Fox, NVIDIA senior director of robotics research and professor at the University of Washington Paul G. Allen School of Computer Science and Engineering.
“In the past, robotics research has focused on small, independent projects rather than fully integrated systems,” Fox said. “We’re bringing together a collaborative, interdisciplinary team of experts in robot control and perception, computer vision, human-robot interaction and deep learning.”
The new lab comes only two months after the company opened another research facility in Toronto, increasing its Canadian team to 50 researchers. Led by Sanja Fidler, a professor at the University of Toronto and deep learning and computer vision expert, the lab is focused on exploring AI and deep learning possibilities.
The new Seattle lab opened its doors with 14 researchers, but the company forecasts tripling the staff during the next six months. While AI will also come into play, the goal of this facility will be more focused on the real-world issues with robotics and development of the next generation of robots: cobots.
Whereas current industrial manipulators complete repetitive tasks and are kept separate from humans, cobots are designed to work alongside humans while performing more complex tasks. This is good news for industries, such as manufacturing and health care, which could benefit from the collaboration, safety, ease of use and enhanced functionality of cobots.
“We want to develop robots that can naturally perform tasks alongside people,” Fox said. “To do that, they need to be able to understand what a person wants to do and figure out how to help them achieve a goal.”
While the lab already has dozens of projects it’s ready to take on, first up is its “kitchen manipulator.” Seemingly simple, this robot that can operate in a real-life kitchen and uses AI and deep learning to solves tasks such as retrieving objects, learning how to clean, and prepping a meal. The real-world benefits could be tremendous for people needing assistance. Additionally, the principles developed for the kitchen robot could be applied to other industries.
“All of this is working toward enabling the next generation of smart manipulators that can also operate in open-ended environments where not everything is designed specifically for them,” Fox said. “By pulling together recent advances in perception, control, learning and simulation, we can help the research community solve some of the greatest challenges in robotics.”
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