1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…