1 citations · 2 across the 12 of their papers we have counts for
12 papers
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…
Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
Naoki Kiyohara, Edward Johns, Yingzhen Li
Stochastic differential equations (SDEs) are well suited to modelling noisy and irregularly sampled time series found in finance, physics, and machine learning. Traditional approac…
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…