Physical AGI is bottlenecked on data that does not exist yet. CapturGO collects egocentric, non-synthetic capture through a distributed contributor network, under standardized protocols with quality assurance on every clip.
First person capture from real environments, with the task context, action labels, and metadata a model needs to learn from it.
Egocentric completions of real manipulation tasks, captured in the environments where those tasks actually happen.
Variation across objects, spaces, lighting, and people, which is what separates a usable dataset from a narrow one.
What people do when a task does not go cleanly, so a policy learns more than the ideal path.
Every capture is traceable to who collected it, under what agreement, and for what use.
Collection is the easy half. Everything after it is why the data is usable.
Contributors capture egocentrically against a task taxonomy, following a standardized protocol for setup, framing, and safety.
Every clip is checked against measurable acceptance criteria before it counts. Rejected captures are not paid or delivered.
Task context, action labels, and environment metadata are attached, which is what makes a clip trainable.
Embodiment agnostic datasets for post-training and evaluation of embodied AI systems, shaped to the task coverage a team actually needs.
A contributor network means continuous small payments to people wherever they are. CapturGO does not run a payments team. It calls Okuru, the wallet infrastructure built by the same company.
Okuru →