Por João Bimbo (LASIGE/DI-FCUL).
This seminar explores an intersection of robotics and data science by focusing on probabilistic models for state estimation. State estimation is a critical component in robotics, enabling robots to understand their environment and make informed decisions in scenarios where information may be limited or noisy. Similarly, in data science, understanding the state of a system can be crucial for predictive analytics and decision-making. The aim of the seminar is to equip students with the understanding and tools to implement probabilistic models that, although originally developed in the field of robotics, are applicable to their own area. We will explore Bayesian Recursive Estimation techniques, such as Kalman Filters and Particle Filters, and understand how these can be applied to data analytical tasks. Applications in robot navigation and manipulation will be demonstrated.
Short bio: João Bimbo is an invited assistant professor at FCUL and a researcher at LASIGE. He has a MSc in Electrical Engineering from University of Coimbra (2011) and a PhD in Robotics from King's College London (2016). His research interests are in robot manipulation, sensing, and haptics.
