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4 papers
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…