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Third Seminar in 2023-2024 Seminar Series

We're excited to continue our virtual seminar series for the TC on Model-Based Optimization for Robotics.

We'll have a talk from Noémie Jacquier from Karlsruhe Institute of Technology (KIT) at 10AM EST February 23rd 2024 (Friday). Please find the flyer with all the details at this link.

Speaker: Noémie Jacquier (Karlsruhe Institute of Technology (KIT))

Title: From Data Structure, Physics, and Human Knowledge: A Manifold of Robotic Geometries

Abstract: To be deployed in our everyday life, robots must display outstanding learning and adaptation capabilities allowing them to act, react, and continuously learn in unstructured dynamic environments. In addition, robots should display such capabilities in real time, which entails the ability to continuously learn from small numbers of demonstrations and/or interactions. In this context, the quality and efficiency of robot learning approaches may be improved via the introduction of inductive bias. In this talk, I will view inductive bias through the lens of geometry, which is ubiquitous in robotics. Specifically, I will discuss via three examples how geometry-based inductive bias can be introduced into robot learning from data structures, from physics, and from human knowledge. First, I will show that the performance of various algorithms may be improved by considering the intrinsic geometric characteristics of data. Second, I will discuss how the dynamic properties of humans and robots are straightforwardly accounted for by considering physics-based geometric configuration spaces. Finally, I will show that imposing an additional geometric structure to probabilistic latent spaces allows us to learn low-dimensional representations of robotics taxonomies in continuous domains from which we can generate realistic motions.

Date: Friday, February 23rd 2024

Time: 10:00-11:00 AM EST (GMT -05:00)


More details on upcoming seminars (and video links for past ones) can be found here.