Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models

• URL: http://arxiv.org/abs/2312.04533v2
• Date: Mon, 29 Jul 2024 20:40:09 GMT
• Title: Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models
• Authors: Ivan Kapelyukh, Yifei Ren, Ignacio Alzugaray, Edward Johns,
• Abstract summary: 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.
• Score: 12.965144877139393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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