1 citations · 2 across the 12 of their papers we have counts for
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Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation
Yilong Wang, Cheng Qian, Edward Johns
Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multi…
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…
Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting
Yilong Wang, Cheng Qian, Ruomeng Fan +1
We propose Observer Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct o…
Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
Yifei Ren, Edward Johns
Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics. In this work, we show that 3D gen…
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…
MILES: Making Imitation Learning Easy with Self-Supervision
Georgios Papagiannis, Edward Johns
Data collection in imitation learning often requires significant, laborious human supervision, such as numerous demonstrations, and/or frequent environment resets for methods that…
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…
Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models
Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray +1
We introduce Dream2Real, a robotics framework which integrates vision-language models (VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the rob…
SceneScore: Learning a Cost Function for Object Arrangement
Ivan Kapelyukh, Edward Johns
Arranging objects correctly is a key capability for robots which unlocks a wide range of useful tasks. A prerequisite for creating successful arrangements is the ability to evaluat…
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…
One-Shot Imitation Learning: A Pose Estimation Perspective
Pietro Vitiello, Kamil Dreczkowski, Edward Johns
In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowle…