Seminario organizado en el marco del programa doctorado en Ciencia y Tecnología Informática y el Máster Universitario en Ciencia y Tecnología Informática.
Learning Technologies for Robot Autonomy through Imitation and Reinforcement (web)
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Ponente: Roberto Martin-Martin, University of Texas at Austin. Lugar: Aula 2.0.C03 Fechas: 17, 18 y 19 de Marzo Sesiones:
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Organizador: Fernando Fernández
Idioma: Inglés
Resumen:
In this seminar, we will cover some of the fundamentals of machine learning applied to robotics including techniques such as Imitation Learning (supervised and self-supervised), Reinforcement Learning and the use of Foundation Models. We will have a brief overview of the basics of those techniques and a deep dive into their application in real-world robotic tasks. The seminar will also include some fundamentals of computer vision and their application to robotics and robot learning.
Breve biografía:
Roberto Martin-Martin is Assistant Professor of Computer Science at University of Texas at Austin. His research connects robotics, computer vision and machine learning. He studies and develops novel AI algorithms that enable robots to perform tasks in human uncontrolled environments such as homes and offices. In that endeavor, he creates novel decision-making solutions based on reinforcement learning, imitation learning, planning and control, and explores topics in robot-perception such as pose estimation and tracking, video prediction and parsing. Martin-Martin received his Ph.D. from Berlin Institute of Technology (TUB) prior to a postdoctoral position at the Stanford Vision and Learning Lab under the supervision of Fei-Fei Li and Silvio Savarese. His work has been selected as RSS Best Systems Paper Award, RSS Pioneer, Winner of the Amazon Picking Challenge, and ICRA and IROS Best Paper Nominee. He is chair of the IEEE Technical Committee in Mobile Manipulation.
