Singapore-based Ropedia raises US$22mil in pre-series A, aims to scale data infrastructure for physical AI

• US$30mil total funding raised for real-world interaction data platform for robotics, embodied AI systems
• Academic deep-tech spinout from NTU aims to be the foundational data infrastructure for physical AI era

Ropedia, a Singaporean startup building data infrastructure for physical AI, announced today its pre-Series A funding, bringing its total funding to US$30 million (SG$ 38.7 million). The US$22 million (SG$28.4 million) round was led by venture investors with deep experience in AI, deep tech technology, and infrastructure in Southeast Asia and follows a previous raise in which investors and angels connected to Google, a16z, NVIDIA, and Amazon participated.

Ropedia’s vision is to be the foundational data infrastructure for the physical AI era. “Just as cloud computing required data centers, and language AI required the internet’s text — physical intelligence requires massive, high-quality, real-world interaction data”, said Chen Zhaoxi, co-founder and CEO.

Investors have no doubt been attracted to the academic credentials of the three founders of Ropedia. Considered an academic deep-tech spinout, which is rare in Asia, its founders – Chen, Fangzhou Hong (CTO) and Asso Prof Liu Ziwei (Chief Scientist) also share some common academic background.

Chen and Hong both did their undergraduate studies at Tsinghua University, China where Chen majored in Automation while Hong majored in Software Engineering, before both pursued PhDs at Nanyang Technological University in Singapore where both were based within the MMLab research ecosystem and worked on areas such as 3D perception, human reconstruction, spatial intelligence and egocentric vision.

Their professional experience also overlaps. Both Chen and Hong completed research internships at Meta Reality Labs, where they worked on technologies related to avatars, first-person perception and human-centred spatial computing.

At NTU, their PhD supervisor was Liu, who is an associate professor and computer-vision researcher at NTU. Liu did his undergrad in China too where he studied at Huazhong University of Science and Technology before earning his PhD from the Chinese University of Hong Kong.

Ropedia said it plans to invest in expanding global data collection, growing its Singapore and US teams, and increasing manufacturing of wearable capture hardware to support larger fleet deployments. The company also plans to build out its data platform, adding annotation tooling, quality analytics and compliance infrastructure, and to grow its AI research team’s work on data foundation models and world models.

“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. Our technology lets robots capture the experience and real judgment it will take for them to move through the world. If we get all that right, the robots will leave the beta stage and start doing real work, first in factories, then at home, with families,” said Chen.

Its platform spans the entire Physical AI data pipeline, from capturing multimodal human experience data to delivering model-ready datasets. Using its proprietary wearable hardware, the company captures egocentric video, depth, motion and audio before synchronizing and processing the data through its platform. The resulting datasets are delivered through Xperience-10M, one of the world’s largest Human Experience Datasets, or as custom Data-as-a-Service (DaaS) offerings for robotics and embodied AI developers.

For robotics and Physical AI developers, the expansion will translate into faster access to larger, more diverse datasets collected across a broader range of real-world environments, tasks, and geographies. By increasing both the scale and diversity of human experience data, Ropedia aims to help customers build AI models that generalize more effectively beyond controlled laboratory settings and into real-world deployment.

Ropedia claims that its approach cuts data-collection costs by up to 50x compared with traditional methods, while its wearable capture device has entered mass production to support larger-scale deployments. The company claims to serve more than 20 robotics and foundation model companies across North America, China, and Singapore in the fields of embodied AI and spatial intelligence.

By building an end-to-end platform for capturing, processing, and delivering human experience data at scale, Ropedia said it aims to provide the infrastructure layer underpinning the next generation of robotics and embodied AI. As Physical AI moves from research labs into factories, workplaces, and homes, the company believes scalable real-world data will become the foundation on which the industry’s next wave of innovation is built.