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2210.01911.pdf
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🔗 Source: arxiv.org
📊 Size: 4.68 MB
📄 Pages: 9 pages
⬇️ Downloads: 35

Summary

Researchers propose a novel approach to efficiently learn general-purpose language-conditioned robot skills from unstructured, offline, and reset-free data in the real world. They exploit a self-supervised visuo-lingual affordance model, requiring annotation of as little as 1% of the total data with language. The method, called HULC++, achieves state-of-the-art performance on the CALVIN benchmark and learns over 25 distinct visuomotor manipulation tasks with a single policy in the real world. When paired with Large Language Models, HULC++ enables completing long-horizon, multi-tier tasks from abstract natural language instructions, requiring an order of magnitude less data than previous approaches.

Description

Researchers propose a novel approach to efficiently learn general-purpose language-conditioned robot skills from unstructured, offline, and reset-free data in...

Technical Information

  • File Format: PDF
  • File Size: 4.68 MB
  • Pages: 9
  • Language: EN
  • Total Downloads: 35
  • Last Updated: 3 weeks ago

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