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Abstract: Much has been said about the need for diversity in robotics. From diverse datasets for training large vision-action models to diverse motion 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 this talk, I will provide a probabilistic interpretation for diversity and show that 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. I will describe a powerful nonparametric inference method that uses both differentiability and parallelism to provide nonparametric posterior approximations for model predictive control, motion planning, and state estimation. 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. 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 200 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).Zoom Link: https://illinois.zoom.us/j/83068322043?pwd=McN371foR4WvsLrEIafIVDurVN5kgT.1
Meeting ID: 830 6832 2043
Password: 746892