EgoVerse and Newton are emerging as the two global infrastructure layers for embodied AI
量子位 · wechat · 2026-07-23
EgoVerse and Newton are becoming the two global bases for embodied AI
This long post argues that embodied AI is now coalescing around two infrastructure layers:
- EgoVerse: a growing first-person human-operation data ecosystem for robot learning
- Newton: a physics simulation foundation for robot training and evaluation
EgoVerse in numbers
- 1,362 hours of human demonstration data
- About 80,000 episodes
- 1,965 tasks, 240 scenes, and 2,087 contributors
What the ecosystem is trying to solve
- Standardize how human demonstrations are collected and shared
- Make data portable across labs, robots, and task definitions
- Validate whether human-first data actually transfers into robot skills
Who is involved
- Universities: Georgia Tech, Stanford, ETH Zurich, UC San Diego
- Companies: Meta, Scale AI, and several embodied-AI startups
- A Chinese company, Guanglun, is highlighted as the only participant appearing in both EgoVerse and Newton
Why Guanglun matters
The post says Guanglun contributes a full quality loop for human data:
- edge-side agent monitoring during collection
- unified cloud processing across hardware types
- VIO, hand/body annotation, and action semantics
- simulation and benchmark validation to check whether the data is worth training on
The broader point: embodied AI is moving from ad hoc demos to shared infrastructure, and data plus simulation are becoming the new global standards.
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