Ropedia raises $22M to scale data collection for training robots

Ropedia’s HOMIE is a lightweight device that includes four cameras and a swappable battery module. | Source: Ropedia Ropedia, which is building data infrastructure for physical AI, today said it has raised $22 million in pre-Series A funding. Combined with the company’s seed round, it has raised $30 million.

The company plans to use the funding to scale HOMIE, its wearable , head-mounted device. HOMIE captures first-person human movement, object interaction, and spatial context and then feeds that data into Ropedia’s processing and annotation models. “For this round of fundraising, we’re actively expanding our business and technical networks in the sense that we want to build up our teams in hardware, software, data infrastructure, and model development,” Zhaoxi Chen, co-founder and CEO of Ropedia, told The Robot Report .

” Ropedia 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. Founded in 2025, the company has offices in Singapore and Mountain View, Calif. The robotics industry needs data infrastructure The data pipeline that starts with HOMIE runs end to end, from capture to model-aligned fine-tuning.

This creates data that Ropedia generates and structures itself rather than the client-owned data that most data-labeling providers annotate. That full-stack approach also sets it apart from teleoperation -based capture, which requires physical robot hardware and is typically limited to specific robot embodiments. Chen asserted that Ropedia is building the data infrastructure the robotics industry needs if it wants to scale.

“Infrastructure means you’re going to be able to process massive scale of data in a short time,” he said. ” What is HOMIE? HOMIE is a data-collection device equipped with 360º camera view.

Ropedia aims to build human-level intelligence in robotics, and to do this, Chen claimed that the best place to start is with copying human behavior. “You’re going to mimic human-level intelligence by going back to the way humans learn new skill sets,” he said. “We learn in the physical world through our vision, through our interaction from a first-person perspective.

We learn from a first-person view. We learn all physical skills or high-level reasoning semantic skill sets from this perspective. ” HOMIE includes other sensing modalities in addition to vision .

“We also have an audio sensor where you can reconstruct the source and direction of an audio signal in the environment,” Chen said. ” Part of what helps Ropedia stand apart, Chen said, is that the company developed much of HOMIE in-house. This custom hardware results in better data.