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Yingsu opens China's first embodied-AI simulation data collection center in Hangzhou

10/30/2026 — 10/30, 21:20·1 sources (all)·1 reports (all)

Key takeaways

  • According to Leiphone, Yingsu has opened what it calls China's first embodied-AI simulation data collection center in Hangzhou, shifting dat…

Background & analysis

According to a report by Leiphone on 2026-10-30 at 19:08, Yingsu has opened the country's first embodied simulation data collection center in Hangzhou. The center was first disclosed by this outlet.

The report describes the center as an attempt to shift embodied AI data production from a human-hours model to one based on environment configurations multiplied by parallel compute, meaning training data is batch-generated by configuring many simulated environments and running them in parallel rather than collected person by person.

Citing figures from the report: training one usable embodied AI model requires tens of millions of hours of multimodal interaction data, while the industry has accumulated only one million hours over two years, and less remains usable once low-quality material is filtered out. The center plans to produce that one million hours of data within a single month, targeting the shortage of training data for embodied models.

As of this report, the publicly available facts are limited to the center's opening in Hangzhou, its positioning as a national first in embodied simulation data collection, and the one-month production target. Details on actual production lines, partners, and a deployment schedule have not been reported.

AI-generated from 1 reports · updated 6 days ago

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Reports on this story headlines open the original

Oct 30
  1. According to Leiphone, Yingsu has opened what it calls China's first embodied-AI simulation data-collection center in Hangzhou, shifting data production from "people × hours" to "environment configurations × parallel compute." The report says the center aims to produce in one month the million hours of data the industry accumulated over two years, addressing a shortage of embodied-model training data.

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