We’re archiving the physical world for embodied intelligence by collecting and labeling aligned multimodal data. To build dexterous and perceptive robots that generalize robustly, we need massive amounts of real-world data across multiple modalities and environments.
We have thought deeply about the fine line between biomimicry and its application to humanoid systems. Based on this research, we design and deploy custom hardware across residential and manufacturing settings. We then post-process the resulting data through internal QA, anonymization, and annotation pipelines to deliver diverse, high-fidelity datasets at scale to frontier labs developing robotics foundation models and general-purpose robotics companies.
We believe we are at a historic inflection point, with a unique opportunity to leave a dent on humanity and reshape physical labor markets forever. That's why our team dropped out of Stanford and Berkeley and moved to Asia to collect the world’s largest annotated multimodal dataset.