Exclusive: In the race for robot training data, ex-Meta researchers’ startup turns to gig workers with cameras
The San Francisco startup supplies first-person video data for robot training, with cameras capturing the hands-on details of preparing food, stocking shelves and repairing equipment.
Key points
- Physical Intelligence has tested combining human video with teleoperation data, which is recorded as people control robots remotely, demonstrating how to complete tasks or handle unfamiliar objects and environments.
- Physical Intelligence co-founder Sergey Levine wrote in a blog post that human video and other supplementary data could provide “a source of knowledge about what can happen in the real world,” complementing data collected by robots themselves.
- For a robot learning to make coffee, those recordings could show how someone grips a cup, positions it beneath a grinder and moves it without spilling.
The company says its customers include three of the top five frontier AI labs, alongside robotics developers buying its first-person training data, although the company declined to name these customers citing non-disclosure agreements.
Additional details
- The company was founded by former Meta researchers Shawn Shen and Ben Zhou.
- The company is launching Memories Upload, a global gig-work platform where contributors take paid video-recording tasks to help train robots.
- “What stands out is the focus on making everyday human experience searchable and useful for robot learning,” Zhuang said of Memories.ai.
- The company has raised $16 million in venture capital funding to date, including an $8 million seed round in July 2025 led by Susa Ventures, with investors including Samsung Next, and an $8 million “seed extension” in June this year.
- Adam Scraba, Nvidia’s head of physical AI ecosystem for vision agents, told Fortune that the wide variety of specialized tasks in heavy industry, coupled with the need for highly accurate models, presents one of the field’s hardest challenges.
How this summary was made: extractive summary — built from the sources' own reporting. It is drawn only from the reporting of the sources listed below — read the originals for full detail. How we summarize →
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