DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics

• URL: http://arxiv.org/abs/2210.02438v3
• Date: Thu, 4 May 2023 14:11:50 GMT
• Title: DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics
• Authors: Ivan Kapelyukh, Vitalis Vosylius, Edward Johns
• Abstract summary: 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. We show that this is possible zero-shot using DALL-E.
• Score: 13.87953637017351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that goal image. We show that this is possible zero-shot using DALL-E, without needing any further example arrangements, data collection, or training. DALL-E-Bot is fully autonomous and is not restricted to a pre-defined set of objects or scenes, thanks to DALL-E's web-scale pre-training. Encouraging real-world results, with both human studies and objective metrics, show that integrating web-scale diffusion models into robotics pipelines is a promising direction for scalable, unsupervised robot learning.

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