“One Robot for Every Task”
Monday April 26, 2021 – 5-6 pm (CEST)
The digitization of practically everything coupled with advances in machine learning, the automation of knowledge work, and advanced robotics promises a future with democratized use of machines and wide-spread use of AI, robots and customization. While the last 60 years have defined the field of industrial robots, and empowered hard bodied robots to execute complex assembly tasks in constrained industrial settings, the next 60 years could be ushering in our time with Pervasive robots that come in a diversity of forms and materials, helping people with physical and cognitive tasks. However, the pervasive use of machines remains a hard problem.
How can we accelerate the creation of machines customized to specific tasks? Where are the gaps that we need to address in order to advance the bodies and brains of machines? How can we develop scalable and trustworthy reasoning engines?
In this talk I will discuss recent developments in machine learning and robotics, focusing on about how computation can play a role in (1) developing Neural Circuit Policies, an efficient approach to more interpretable machine learning engines, (2) making machines more capable of reasoning in the world, (3) making custom robots, and (4) making more intuitive interfaces between robots and people.
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, and Deputy Dean of Research in the Schwarzman College of Computing at MIT. Rus’ research interests are in robotics and artificial intelligence. The key focus of her research is to develop the science and engineering of autonomy.
Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences. She is a senior visiting fellow at MITRE Corporation. She is the recipient of the Engelberger Award for robotics. She earned her PhD in Computer Science from Cornell University.
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