no code implementations • 28 May 2024 • Vitalis Vosylius, Younggyo Seo, Jafar Uruç, Stephen James
In the field of Robot Learning, the complex mapping between high-dimensional observations such as RGB images and low-level robotic actions, two inherently very different spaces, constitutes a complex learning problem, especially with limited amounts of data.
no code implementations • 18 Oct 2023 • Vitalis Vosylius, Edward Johns
Consequently, we show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations, without any prior knowledge about the object class or any further training.
no code implementations • 12 Dec 2022 • Vitalis Vosylius, Edward Johns
Robot learning provides a number of ways to teach robots simple skills, such as grasping.
no code implementations • 5 Oct 2022 • Ivan Kapelyukh, Vitalis Vosylius, Edward Johns
We introduce the first work to explore web-scale diffusion models for robotics.
1 code implementation • 13 Aug 2020 • Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary
In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.