Ropedia, a Singapore-based startup, has raised $30 million in pre-A funding to expand its wearable data infrastructure for physical AI systems, addressing a fundamental problem: robots can't learn physical tasks from videos alone, they need synchronized multimodal data capturing sight, sound, touch, and motion together. The funding will accelerate global expansion of the company's real-world interaction data platform, which serves robotics and embodied AI companies across North America and Southeast Asia.

Why Can't Robots Learn Like Humans Do From Video?

The core insight driving Ropedia's approach is deceptively simple but profound. A robot cannot learn to play baseball by watching a video any more than a human could learn to ride a bike by reading about it. Physical understanding requires embodied experience: the feel of gripping a bat, the timing of a swing, the feedback from impact. This is where most current AI data collection methods fail. They capture isolated streams of information, but physical AI depends on correlating perceptions and actions in real time, with precise temporal alignment.

Ropedia's solution centers on HOMIE, a wearable, head-mounted device that captures first-person video, audio, depth, hand tracking, gaze, body motion, and camera pose simultaneously. Each data stream is timestamped with microsecond precision, ensuring that every sensory input aligns perfectly with the corresponding action. This synchronized multimodal approach sets HOMIE apart from traditional teleoperation-based data collection, which relies on expensive robot hardware and is typically limited to specific robot types.

How Does Ropedia's Data Infrastructure Differ From Traditional Labeling Services?

Ropedia operates as a data infrastructure company, not a data-labeling service. Standard labeling providers annotate data that already exists; Ropedia generates the data itself, then synchronizes, structures, and continually refines it end-to-end. The company runs what it calls a "closed-loop pipeline" that integrates multimodal synchronization and rigorous quality assurance into dataset generation and model-aligned fine-tuning.

This distinction matters because it enables a fundamentally different scaling model. HOMIE can be deployed anywhere and worn by anyone, giving Ropedia a scalable way to collect synchronized, multimodal data in parallel across many environments and users. Unlike teleoperation-based systems, which are constrained by expensive robot fleets, the platform scales simply by adding wearable capture devices. The result is a 50-fold reduction in data-collection costs compared with traditional methods.

What Data Has Ropedia Already Collected?

The company has built Xperience-10M, one of the largest human-experience datasets in the industry, comprising 10 million interaction episodes and more than 10,000 hours of multimodal recordings. This dataset spans billions of synchronized video, depth, motion-capture, and inertial-sensor frames. Each new HOMIE deployment expands the range of environments, behaviors, and interactions the dataset captures, making it more valuable as the network grows. Ropedia has already served more than a dozen North American companies in embodied AI and spatial intelligence.

How to Leverage Multimodal Data for Physical AI Development

• Dataset Licensing: Companies can license access to Xperience-10M and future datasets without investing in their own data collection infrastructure, accelerating model development timelines.
• Selective Hardware Access: Organizations can deploy HOMIE devices to collect domain-specific data tailored to their robotics or embodied AI applications, with Ropedia handling synchronization and quality assurance.
• Research Collaboration: Ropedia partners with leading robotics and foundation-model companies on joint research initiatives, combining proprietary datasets with cutting-edge model development.

The funding breakdown reveals investor confidence in this approach. The latest round raised $22 million, following an earlier $8 million round announced in March, for a total of $30 million. Ropedia will direct the funding toward three priorities: expanding data collection across Southeast Asia and North America, scaling hardware deployment to support larger fleets of HOMIE devices, and advancing its AI research and data platform work, including new engineering hires in the United States.

"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," said Zhaoxi Chen, chief executive and co-founder of Ropedia.

Zhaoxi Chen, Chief Executive and Co-founder at Ropedia

One of Ropedia's angel investors, a research scientist from Amazon, emphasized the team's execution speed and technical depth. "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 stated.

Ropedia's founding team brings substantial expertise to the challenge. Zhaoxi Chen is known for pioneering work in 3D computer vision and multimodal AI. Fangzhou Hong, chief technology officer, previously worked on Meta's egocentric multimodal intelligence research before contributing foundational research in 3D spatial intelligence. Ziwei Liu, chief scientist and an associate professor at Nanyang Technological University in Singapore, rounds out the leadership.

The broader context underscores why this funding matters. Text scraped from the internet trained the previous generation of large language models. Real-world human experience, captured at the same scale and with the same precision, will train physical AI systems. Physical AI promises to move robotics and embodied AI out of the lab and into real-world applications, first in factories, then eventually in homes, helping families with everyday tasks. Ropedia positions itself as the data infrastructure layer for this transition, comparable to the data centers that underpinned cloud computing or the internet text that enabled language models.

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