Learning Model-based Control for (Aerial) Manipulation

Roberto Calandra

Abstract: In this talk, I will discuss what I consider two of the crucial challenges of manipulation: the use of tactile sensors, and model-based control. ‌Humans make extensive use of touch, but integrating the sense of touch in robot control has traditionally proved to be a difficult task. As an alternative, we propose the use of data-driven machine learning methods, to learn complex multi-modal models from raw sensor measurements. I will conclude by discussing useful lessons we learned along the way, and how some of these lessons could be valuable in the context of aerial manipulation.

Bio: Roberto Calandra is a Research Scientist at Facebook AI Research. Previously, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research Laboratory (BAIR) working with Sergey Levine. His education includes a Ph.D. from TU Darmstadt (Germany) under the supervision of Jan Peters and Marc Deisenroth, a M.Sc. in Machine Learning and Data Mining from the Aalto university (Finland), and a B.Sc. in Computer Science from the Università degli studi di Palermo (Italy).

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