90 citations · 114 across the 9 of their papers we have counts for
7 papers · 1 filter
Learning a Thousand Tasks in a Day
Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius +1
Humans are remarkably efficient at learning tasks from demonstrations, but today's imitation learning methods for robot manipulation often require hundreds or thousands of demonstr…
Instant Policy: In-Context Imitation Learning via Graph Diffusion
Vitalis Vosylius, Edward Johns
Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Ins…
Adapting Skills to Novel Grasps: A Self-Supervised Approach
Georgios Papagiannis, Kamil Dreczkowski, Vitalis Vosylius +1
In this paper, we study the problem of adapting manipulation trajectories involving grasped objects (e.g. tools) defined for a single grasp pose to novel grasp poses. A common appr…
Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning
Vitalis Vosylius, Younggyo Seo, Jafar Uruç +1
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, co…
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment
Vitalis Vosylius, Edward Johns
Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objec…
Where To Start? Transferring Simple Skills to Complex Environments
Vitalis Vosylius, Edward Johns
Robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefor…
DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
Ivan Kapelyukh, Vitalis Vosylius, Edward Johns
We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of…