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NVIDIA's Jim Fan touts robot learning via human data, not teleoperation

By PulseAugur Editorial · [1 sources] ·

NVIDIA researcher Jim Fan highlighted EgoVerse, an ecosystem for robot learning derived from human egocentric data. This approach moves beyond traditional teleoperation, focusing on scaling robot learning through behavior cloning. The EgoVerse dataset, developed across multiple research and industry partners, already contains over 1300 hours of data covering 240 scenes and 2000 tasks. AI

IMPACT Accelerates robot learning research by providing a large-scale dataset and a framework for behavior cloning.

RANK_REASON Research announcement detailing a new dataset and approach for robot learning.

Read on X — Jim Fan (NVIDIA) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

NVIDIA's Jim Fan touts robot learning via human data, not teleoperation

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Research announcement detailing a new dataset and approach for robot learning.
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COVERAGE [1]

  1. X — Jim Fan (NVIDIA) TIER_1 English(EN) · Jim Fan ·

    Teleop is so 2025. Ever since we unveiled EgoScale and the dexterity scaling law, it's been clear to us and the ecosystem that behavior cloning direct...

    Teleop is so 2025. Ever since we unveiled EgoScale and the dexterity scaling law, it's been clear to us and the ecosystem that behavior cloning directly from humans is the way to break the curse of teleop. 2026 is all about scaling robot learning without robots.<div class="rsshub…

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