AI
July 28, 2026

Ropedia ropes in $22M Pre-Series A to build data infrastructure for physical AI

Image from Ropedia

Let’s get physical! Ropedia is a Singapore-founded startup that gives robotics and embodied AI systems structured records of real human experience for training and simulation. 

  • And now, they’ve just raised a $22M Pre-Series A, bringing their total funding to $30M!
  • The core team is from Tsinghua, NTU, MMLab, Meta Reality Labs, and Google.

Investor check. The round was led by leading venture investors with experience in AI, deeptech, and infrastructure in SEA.

  • A previous round involved investors and angels connected to Google, a16z, NVIDIA, and Amazon.

👑 The ABCs of Ropedia

Ropedia offers end-to-end data infrastructure for physical AI — from capturing human experience data to delivering model-ready datasets and deployment systems.

Their core offerings include:

  • Dataset licensing. Providing model-ready datasets (like Xperience-10M, captured via their HOMIE head-mounted hardware) to robotics, embodied AI, and world-model teams for training.
  • Annotation infrastructure deployment. Remote or on-premise deployment of multimodal annotation workflows.
  • Selective hardware access. Granting research institutions and enterprise partners targeted access to HOMIE hardware.
  • Research collaborations. Partnering with academic and industrial leaders to push physical AI capabilities forward.

Run the numbers! According to a statement, Ropedia’s work had over 2.6M open-source downloads, over 69K research citations, and more than 40 frontier AI lab engagements. 

🔼 What’s with the $$$? 

They’ll use the capital to expand global data collection into SEA and North America. 

  • They’ll also grow their Singapore and US teams, boost their manufacturing capacity, expand global data collection operations, and scale their data platform to deliver larger, more diverse datasets.

Big plans ahead. They’ll further develop their data platform to add annotation tooling, analytics, and compliance infrastructure. 

  • Oh, and they’ll build on their AI team’s work on data foundation and world models.

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