Hello,

Please join us in person or online for a seminar by Professor Fabio Ramos, more information below.

In-Person: Seminar Room CB11.06.408
Hybrid: Zoom Meeting ID: 864 8484 2636
Please note in-person attendance is recommended as room is not hybrid and will be streamed via a laptop link.

Probabilistic Robotics 2.0: Leveraging Differentiability and Parallelism for Diversity in Planning and Perception under Uncertainty

Professor Fabio Ramos
NVIDIA/ University of Sydney

Abstract: Much has been said about the need for diversity in robotics. From diverse datasets for training large vision-action models to diverse planners that can infer multi-modal trajectories, the word diversity has been a common theme in the last few years of robotics research. But how do we define or even measure diversity in robotics? In this talk, I will provide a probabilistic interpretation for diversity and show that modern tools designed for deep learning such as differentiable programming languages and parallel computation in GPUs can be conveniently utilized for large-scale probabilistic inference that naturally captures the notion of diversity. Specifically, I will describe a powerful nonparametric inference method that uses both differentiability and parallelism to provide nonparametric posterior approximations for problems such as model predictive control, motion planning, state estimation, simulator parameter estimation and more. Finally, I will define diversity in trajectory planning in terms of a new mathematical tool–signature transforms–and how it can lead to novel planning methods in the future. 

Bio: Fabio Ramos is a Professor in robotics and machine learning at the School of Computer Science at the University of Sydney and a Principal Research Scientist at NVIDIA. He received the BSc and MSc degrees in Mechatronics Engineering at University of Sao Paulo, Brazil, and the PhD degree at the University of Sydney, Australia. His research focuses on statistical machine learning techniques for large-scale Bayesian inference and decision making with applications in robotics, mining, environmental monitoring and healthcare. Between 2008 and 2011 he led the research team that designed the first autonomous open-pit iron mine in the world. He has over 150 peer-review publications and received Best Paper Awards and Student Best Paper Awards at several conferences including International Conference on Intelligent Robots and Systems (IROS), Australasian Conference on Robotics and Automation (ACRA), European Conference on Machine Learning (ECML), and Robotics Science and Systems (RSS).