StableBrowse

Teams building physical AI and embodied agents need examples of hands using tools, objects changing state, bodies moving through space, and people recovering from messy physical interactions. The internet does not contain enough of this kind of footage, and scraped video lacks the synchronized sensor data, calibration, and structured labels a model team needs. Without a dedicated data infrastructure layer, companies are left with scattered clips rather than training-ready, reviewable datasets covering capture protocol, sensor sync, calibration, depth, hand tracking, and action labels.

StableBrowse handles the full physical-world data pipeline: multimodal capture of real tasks using egocentric RGB, stereo depth, IMU, audio, hand pose, tactile gloves, and tool/environment context. It post-processes raw recordings into aligned outputs including depth, hand tracking, hand mesh, camera trajectory, object state, and temporal action labels (grasp, lift, move, place, adjust, inspect, recover). Every episode is validated and delivered with synchronized timestamps, calibration, schema-valid labels, review UI assets, manifests, and provenance, and the company also builds custom collection and post-processing protocols for specific physical skills, environments, tools, or sensor stacks a client's model needs.