90 citations · 91 across the 5 of their papers we have counts for
7 papers
ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim
Ivan Kapelyukh, Xiaohan Zhang, Stephen James +2
We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demonstrations, zero policy pre-tra…
Test-Time Spatial Reasoning for Robot Manipulation Using Generative Real-to-Sim
Ivan Kapelyukh, Yafei Hu, Ran Gong +6
Spatial reasoning is fundamental to general robot intelligence, as it enables robots to complete long-horizon tasks involving multi-object interaction. We introduce Simify, a train…
AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
Ran Gong, Xiaohan Zhang, Jinghuan Shang +11
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has beco…
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…
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…
My House, My Rules: Learning Tidying Preferences with Graph Neural Networks
Ivan Kapelyukh, Edward Johns
Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variatio…