We capture and structure multi-modal real-world interaction data — synchronized video, depth, pose, language, and sensor streams — for robotics, embodied agents, and world models.
USED BY RESEARCHERS AND ENGINEERS FROM LEADING AI LABS AND UNIVERSITIES
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The most comprehensive synchronized multi-modal dataset for real-world embodied interaction. Built to train robots that understand how humans navigate, grasp, and manipulate the physical world.
Wearable egocentric capture unit for in-the-wild data collection. Lightweight, battery-powered, and optimised for unconstrained real-world environments.
High-fidelity multi-camera rig for controlled manipulation studies. Sub-millimeter pose accuracy with time-of-flight depth and structured light fusion.
Data captured across hundreds of real-world environments — not synthetic scenes. Models trained on ZILLIA generalise to deployment conditions from day one.
Hardware-locked timestamps align RGB, depth, IMU, hand pose, and language streams to under 1 ms. No drift. No post-hoc alignment hacks.
Data ships in MCAP and HDF5 with LeRobot-compatible episode indexing. Plug directly into your training loop with one import.
From sensor capture to cloud-indexed, annotation-complete datasets. We own the full stack — no third-party annotation handoffs, no broken metadata.
Robots fail in the real world not because the models are wrong — but because the data they were trained on was never real. Simulations miss the chaos. Synthetic datasets miss the nuance. Lab recordings miss the scale.
ZILLIA was built to close this gap. We put sensors in the world, capture how humans actually move and interact with objects, and structure that signal into the cleanest possible training substrate for embodied intelligence.
We believe the next generation of physically capable AI will be trained on data that looks like ours.
Whether you're training a foundation model, scaling a robotics product, or exploring embodied AI — we have the data infrastructure to accelerate your work.