Ruben Martinez-Cantin (Instituto de Sistemas e Robótica)

Towards Closing the Loop: Active Learning for Robotics

Abstract:

The ability to adapt to changing environment autonomously will be essential for future robots. While this need is well-recognized, most machine learning research focuses largely on perception and static data sets. Instead, future robots need to interact with the environment to generate the data that is needed to foster real-time adaptation based on all information collected in previous interactions and observations. In other words, we need to close the loop between the robot acting, robot sensing and robot learning. Novel active methods need to outperform passive methods by a margin that compensates the potential of the extra computational burden and the cost of the active data sampling.

In this talk, we present a common framework for active learning in different applications, such as planning, robot localization and mapping, calibration, sensor networks and computer graphics. Our results show that in many applications, active sampling provides an improvement, while in other applications is mandatory to achieve a good performance.

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Bio: Ruben Martinez-Cantin is a postdoctoral researcher at the Institute of Systems and Robotics at IST in Lisbon. Before joining IST, he received a PhD and MSc in Computer Science and Electrical Engineering from the University of Zaragoza in 2008 and 2003, respectively. During his PhD, he worked in the Robotics, Perception and Real Time Group under the supervision of Prof. José A. Castellanos in mobile robotics and Bayesian inference and reasoning. In 2006 and 2007, he has been a visiting scholar at the Laboratory of Computational Intelligence (LCI) at UBC in collaboration with Prof. Nando de Freitas. Previously, he worked as research assistant at University of Zaragoza, in vision based control for mobile robots and intelligent surveillance systems. He also developed some ideas for space robotics and got two grants by the European Space Agency.

His research interests include Bayesian inference, machine learning, robotics, computer vision and cognitive models. He is also interested in the popularization of science.