A New Data Layer for Physical AI
Ropedia, a Singapore-based physical AI data infrastructure company, has raised $30 million across two pre-A funding rounds — $22 million in its latest close plus $8 million secured in March 2026. The financing brought together angel investors, long-term financial backers, and strategic partners with deep expertise in AI, enterprise technology, robotics, mobility, and infrastructure.
The company plans to deploy the capital to expand data collection operations across Southeast Asia and North America, ramp up production of its HOMIE wearable data-capture devices, advance its AI research and data platform, and hire engineering talent in the United States.
Why Data Infrastructure Matters for Embodied AI
Physical AI systems — robots, autonomous machines, and embodied technologies — require training data that connects visual and sensory information with human actions and physical outcomes. Text scraped from the internet trained the last generation of AI models; real-world human experience, captured at scale, will train physical AI.
Ropedia's HOMIE device is a wearable head-mounted system that records first-person video, audio, depth, hand movements, gaze, body motion, and camera position simultaneously. Each data stream is timestamped so developers can understand how perception and movement correspond in real time.
"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," said Zhaoxi Chen, co-founder and CEO of Ropedia.
Team and Background
Ropedia was founded in the second half of 2025 by CEO Zhaoxi Chen, CTO Fangzhou Hong, and Chief Scientist Ziwei Liu. The company is headquartered in Singapore with an office in Mountain View, California.
The funding comes amid a broader boom in data infrastructure for embodied AI, as robotics companies increasingly recognize that high-quality, diverse real-world training data is the bottleneck for general-purpose robot learning.
