Outline and objectives

Domain knowledge and data structures in robotics can be viewed through the lens of geometry. Various robotic variables exhibit distinct geometric characteristics, collected data often lie in curved spaces, and many problems naturally lend themselves to geometric interpretations. In this context, the realms of differential geometry, specifically encompassing Lie groups and Riemannian manifold theories, provide invaluable tools to grapple with the intricate geometry of non-Euclidean spaces. Although geometric methods have been successfully applied to robotics from early on, their recent resurgence within robot learning, control, and optimization has garnered significant attention. Recent research underscores the transformative potential of exploiting geometry-awareness in these robotic challenges, yielding improved performance, data-efficient learning, and robust stability guarantees. 

This tutorial serves a dual purpose: to kindle the interest of the robotics community in geometric methods, often overlooked in robot learning, control, and optimization, and to underscore the critical role of differential geometry across various branches of robotics. By providing a comprehensive introduction to geometric methods, a deep dive into advanced methodologies applied to robotics, and guidance on best practices for incorporating Riemannian geometry, this tutorial equips researchers with essential tools to seamlessly integrate geometry into their work, fostering innovation and

advancements in the field.

Invited speakers

Organizers