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Roberto Calandra
Bio:
Roberto Calandra is a Full (W3) Professor at the Technische Universität Dresden, and at the Centre for Tactile Internet with Human-in-the-Loop. Previously, he founded at Facebook AI Research (now Meta AI) the Robotic Lab in Menlo Park. Prior to that, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research (BAIR) Lab. His education includes a Ph.D. from TU Darmstadt (Germany), 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). His scientific interests are broadly at the conjunction of Robotics and Machine Learning. Roberto served as Program Chair for AISTATS 2020, as Guest Editor for the JMLR Special Issue on Bayesian Optimization, and has previously co-organized over 14 workshops at international conferences (NeurIPS, ICML, ICLR, ICRA, IROS, RSS).
Abstracts:
Perceiving, Understanding, and Interacting through Touch
Touch is a crucial sensor modality in both humans and robots. Recent advances in tactile sensing hardware have resulted -- for the first time -- in the availability of mass-produced, high-resolution, inexpensive, and reliable tactile sensors. In this talk, I will argue for the importance of creating a new computational field of "Touch processing" dedicated to the processing and understanding of touch, similarly to what computer vision is for vision. This new field will present significant challenges both in terms of research and engineering. To start addressing some of these challenges, I will introduce our open-source ecosystem dedicated to touch sensing research. Finally, I will present some applications of touch in robotics and discuss other future applications.
Towards Embodied Artificial Intelligence
To achieve robots capable of meaningfully and safely interacting in our everyday life, it is necessary to achieve a level of adaptability and generalization that is unfeasible with traditional programming paradigms. The use of Artificial Intelligence is a promising alternative. In this talk, I will present recent advances in the field of robot learning and discuss exciting new opportunities and future challenges.
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