Opportunities and Challenges for Scaling Up Robot Learning

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Our next speaker is Abhinav Valada. He is an Assistant Professor for Robot Learning at the University of Freiburg and the Cluster of Excellence BrainLinks-BrainTools. He received his Ph.D. from the University of Freiburg where he subsequently worked as a postdoctoral research scientist and received his MS in Robotics from Carnegie Mellon University. His research interests include robot learning, robot perception, self-supervised and unsupervised learning.

Abhinav will talk about "Opportunities and Challenges for Scaling Up Robot Learning". After the talk, there is time for an open discussion. We invite you to stay and have a get-together. This event is free and open for everyone who is interested in the broad field of AI.

A long-standing vision of robotics has been the creation of autonomous robots that can learn from the world around them and assist humans in performing a variety of tasks including services in our homes, transportation, and rescue in catastrophe-struck environments. However, most of the robots today are programmed to operate in carefully engineered factory settings or they are confined to only perform specific tasks in precisely modeled environments. This can be primarily attributed to the narrow scope of current methods that restrict their ability to quickly adapt to new tasks or previously unseen environments. In this talk, I will first present our state-of-the-art deep learning methods for several fundamental problems that are key enablers for robot autonomy including perception, localization, and prediction. I will then discuss recent advances that we are making in Freiburg for scaling up robot learning through self-supervision.

We are looking forward to seeing you!