Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models
This addresses the challenge of language-conditioned object rearrangement for robotics, offering a zero-shot solution that avoids costly data collection, though it builds incrementally on existing vision-language and 3D rendering techniques.
The paper tackles the problem of enabling robots to rearrange objects based on language instructions without prior training data, achieving zero-shot performance by using vision-language models to evaluate virtual 3D arrangements and select the best one for real-world execution.
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 robot autonomously constructing a 3D representation of the scene, where objects can be rearranged virtually and an image of the resulting arrangement rendered. These renders are evaluated by a VLM, so that the arrangement which best satisfies the user instruction is selected and recreated in the real world with pick-and-place. This enables language-conditioned rearrangement to be performed zero-shot, without needing to collect a training dataset of example arrangements. Results on a series of real-world tasks show that this framework is robust to distractors, controllable by language, capable of understanding complex multi-object relations, and readily applicable to both tabletop and 6-DoF rearrangement tasks.