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

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 ph...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 8; H. 7; S. 3956 - 3963
Hauptverfasser: Kapelyukh, Ivan, Vosylius, Vitalis, Johns, Edward
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
AI-based methods
Big Data in robotics and automation
deep learning in grasping and manipulation
Image segmentation
Pipelines
Predictive models
Robotics
Robots
Task analysis
Training
Visualization
Webs
ISSN:2377-3766, 2377-3766
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3272516