Ropedia Pte. is raising $30 million to scale up its physical AI data infrastructure. is raising $30 million to scale up its physical AI data infrastructure.
Co-founded in late 2025 by Zhaoxi Chen, Fangzhou Hong, and Ziwei Liu, Ropedia’s core mission is to enable AI systems to interact effectively with the physical world. Their approach provides robots with the sensory data they need to master complex tasks, moving beyond theoretical models to practical application.
Rethinking physical AI data collection
Traditional AI relies heavily on vast quantities of internet text and digital images for training. However, physical AI and robotics demand a different kind of input: genuine sensory data. Robots need to interpret tactile pressure, camera angles, body positioning, and timing with millisecond precision.
Ropedia addresses this by generating raw, real-world data directly, rather than simply re-labelling existing datasets. This method is reportedly up to 50 times less costly than classic teleoperation setups. Human data collectors gather natural interaction data in everyday environments, avoiding the expense of dedicated robotic equipment for each trial.
Xperience-10M: a foundational dataset
The company’s flagship dataset, Xperience-10M, is central to its offering. This extensive library contains more than 10,000 hours of multimodal recordings. It also features 10 million interaction episodes, providing a rich training ground for robotics systems.
Xperience-10M includes billions of synchronised frames spanning video, depth information, motion capture, and inertial sensor data. This provides robotics labs with structured loggings of real human actions. Such high-fidelity data is crucial for developing robust, real-world AI applications.
HOMIE: the proprietary capture hardware
At the core of Ropedia’s data engine is HOMIE, a proprietary wearable head-mounted device. This technology captures multiple multimodal data streams concurrently, ensuring precise alignment. The device records first-person video, audio, depth maps, hand tracking, gaze directions, body movements, and camera poses.
Critically, all data streams captured by HOMIE receive accurate timestamps. This level of synchronisation is vital for physical AI models, which require systems to perceive and act with exact timing. Most existing capture tools simply don’t operate at this demanding level.
Precision in multimodal data capture
The ability to precisely timestamp diverse data streams overcomes a significant hurdle in advanced physical AI development. Without this precision, a robot cannot effectively learn the exact timing needed for tasks like gripping tools or assembling electronic components. HOMIE provides this foundational accuracy from the outset.
Its lightweight design, weighing less than half a pound, includes four cameras and a swappable battery module. This makes HOMIE highly portable, allowing data collection in diverse settings like factory floors, kitchens, or repair shops. This versatility is key to generating comprehensive and representative datasets.
Scaling HOMIE for wider deployment
Ropedia has already started mass production of HOMIE devices to supply tech partners and commercial clients. The company plans to ramp up to 10,000 units for large-scale deployments of its second-generation hardware. This manufacturing commitment underscores its ambition to expand data collection efforts.
The widespread deployment of HOMIE will accelerate data capture for its clients globally. Such hardware initiatives are essential to support the increasing demand for high-quality human experience data in robotics development. Infrastructure investment often underpins the adoption of advanced robotics and AI systems.
Funding deployment and global expansion
The US$30 million Pre-A funding arrived in two tranches. An initial US$8 million round closed in mid-March 2026, followed by a larger US$22 million round announced in July. Venture investors with expertise in AI, deep tech, and infrastructure in Southeast Asia led the latest round.
Earlier investors included angels connected to Google, Andreessen Horowitz (a16z), NVIDIA, and Amazon. This capital will primarily fuel the expansion of Ropedia’s data collection fleets across Southeast Asia and North America. It also supports growing teams in Singapore and the US.
Strengthening engineering and research capabilities
A portion of the newly secured funding is earmarked for strategic recruitment, particularly for engineers at its Mountain View, California, office. Building a strong engineering team is crucial for refining the HOMIE hardware. These hires will also enhance the underlying data platform, driving core innovation.
Ropedia will also expand its AI research team, focusing on data foundation models and world models. These areas are essential for the next generation of autonomous systems. Advancing this research will solidify Ropedia’s position at the forefront of physical AI development, creating more intelligent systems for industrial use.
Industrial impact of physical AI data infrastructure
Ropedia’s platform is already serving more than 20 robotics and foundation model companies across North America, China, and Singapore. These firms operate in the fields of embodied AI and spatial intelligence. The data infrastructure provides a critical backbone for developing more capable autonomous systems.
By capturing human action at scale, Ropedia offers the essential foundation for robotics developers. This directly impacts industrial sectors where precise robotic manipulation and nuanced human-robot interaction are increasingly vital. Imagine robots performing delicate assembly tasks or precisely operating tools in demanding environments.
Empowering autonomous systems development
The availability of high-fidelity, real-world data significantly accelerates the development of advanced autonomous systems. Companies utilising Ropedia’s Data-as-a-Service (DaaS) offerings can avoid the substantial costs and complexities associated with establishing their own large-scale data collection. This democratises access to crucial training data, speeding up industry innovation.
For engineers and operations professionals, this translates to a faster path for deploying intelligent robots. These machines will be better equipped to handle unstructured environments and tasks requiring human-like dexterity. Such advancements hold considerable potential for AI banking expansion and other industrial sectors.
Future prospects for physical AI applications
As Ropedia scales its data collection and refines its technology, the implications for physical AI are substantial. The company’s goal is to enable robots to learn complex tasks that currently demand human expertise. This could usher in a new era of automation, boosting productivity and safety across various industrial applications.
The enhanced capabilities of physical AI systems will undoubtedly influence manufacturing processes, supply chains, and the design of industrial facilities. For professionals in these sectors, understanding the advancements in AI infrastructure faults and data provision like Ropedia’s is paramount. It signals a global shift towards more adaptive and autonomous industrial operations, with significant opportunities for regions like Africa.
