Ropedia has raised USD $30 million in pre-A funding. The Singapore-based startup will use the money to expand its data infrastructure for physical AI.
The funding came across two pre-A rounds, with the latest contributing USD $22 million and an earlier round USD $8 million. Backers include venture investors, long-term financial investors, strategic partners in robotics, mobility and enterprise deployment, and angel investors.
Ropedia is building a business around collecting and preparing real-world interaction data for robotics and embodied AI systems. The new capital will support data collection across Southeast Asia and North America, wider deployment of its HOMIE wearable devices, and additional engineering hires in the US.
Its model centres on a head-mounted device called HOMIE, which records first-person video, audio, depth, hand tracking, gaze, body motion and camera pose simultaneously. Each stream is timestamped so developers can link perception and action in real time when training physical AI systems.
This puts Ropedia in a different part of the market from conventional data-labeling providers. Rather than annotating third-party data, it generates its own source material, then processes, synchronises and structures it before licensing datasets, offering selective access to hardware and pursuing research collaborations.
Data pipeline
Ropedia describes its system as a closed-loop pipeline combining multimodal synchronisation, quality assurance and model-aligned fine-tuning. It argues that wearable capture can scale more easily than teleoperation-based data gathering, which depends on robot hardware and can be tied to specific robot formats.
HOMIE has entered mass production and can be deployed across different environments and users. That allows the company to gather data in parallel without relying on expensive robot fleets.
According to the company, its main dataset, Xperience-10M, includes 10 million interaction episodes and more than 10,000 hours of multimodal recordings. It spans billions of synchronised frames from video, depth, motion-capture and inertial sensors.
Ropedia says it has served more than a dozen North American companies working in embodied AI and spatial intelligence. That suggests demand is emerging among developers that need larger volumes of structured physical-world training data as robotics models move beyond controlled lab settings.
Chief Executive Officer and Co-Founder Zhaoxi Chen framed the business around a gap in current AI training methods.
"A robot can't play baseball by watching a video any more than you could learn to ride a bike by reading about it. The robot must understand what it's like to grip a bat and know the timing it takes to hit a ball. That's the information Ropedia's technology provides, and it's why this investment matters. Text scraped from the internet was used to train the last generation of AI. Real-world human experience, captured at the same scale, will train physical AI. Physical AI will let us leave the lab and go to work, first in factories, then at home, helping our families," said Zhaoxi Chen, Chief Executive Officer and Co-Founder of Ropedia.
Investor backing
The company was founded in Singapore in the second half of 2025 by Chen, Chief Technology Officer and Co-Founder Fangzhou Hong, and Chief Scientist and Co-Founder Ziwei Liu, who is also an Associate Professor at Nanyang Technological University in Singapore. Ropedia also has an office in Mountain View, California.
The founders come from computer vision, multimodal AI and spatial intelligence research, fields that have become increasingly relevant as investors look for tools and data sources to support robotics training. Interest in physical AI has grown as companies seek ways to train machines on real-world behaviour rather than on text and image datasets alone.
One of Ropedia's angel investors, identified as a Research Scientist at Amazon, pointed to the team's technical background and commercial progress.
"I backed the Ropedia team early because they had a rare combination of deep technical expertise, speed of execution and a clear vision for where physical AI was heading. Since then, they have built a compelling data infrastructure platform serving leading robotics and foundation-model companies globally. I believe Ropedia is well positioned to become a foundational company in the physical AI ecosystem," the investor said.
Ropedia says its method can reduce data-collection costs by as much as 50 times compared with traditional approaches. If sustained at scale, that could make data supply a more competitive part of the robotics stack. For customers building embodied AI systems, the result is faster access to larger volumes of data across a broader range of tasks and environments.