Today, most robotics data is collected through teleoperation — a human operates a robot, one task at a time. It's slow, expensive, and capped at a 1:1 human-to-robot ratio. Generalizable physical AI stays bottlenecked by data scarcity.
Lili-o replaces human-dependent collection with an industrial-grade autonomous foundry. Powered by our One-Shot/Zero-Shot execution architecture, robots run 24/7, retry on failure, and generate synchronized, contact-rich episodes continuously — with minimal operators per run.

High-fidelity, cross-embodiment data generated directly by autonomous robots. It is the only data that scales models — and no one had found a way to produce it efficiently. Until now.
See how the three data tiers compare →Fewer than 4% of existing high-fidelity datasets contain failure or recovery episodes, and tactile data is nearly non-existent. Robots trained only on flawless trajectories fail the moment they meet minor real-world variation or unexpected slippage.
When a Lili-o robot fails an action, it automatically triggers autonomous recovery loops — capturing the rarest data in the industry: real physical failure and recovery.
| Simulation | Human-Centric | Téléopération | Lili-o | |
|---|---|---|---|---|
| Rich Metadata | Low | Medium | High | High |
| Environment Diversity | High | High | Low | High |
| Price | Medium | Low | High | High |
| Cross-embodiment | No | Yes | No | Yes |
| Scalable | High | Medium | Low | High |
| Companies | Lightwheel · NVIDIA | Scale · Senseirobotic | Tutor · Figure · Agibot | Lili-o |
*EU AI Act compliant
Every episode is an enterprise-ready, synchronized data stream built for direct injection into cutting-edge training pipelines. Force-torque and proprioceptive signals — absent from almost all public datasets — are first-class here.
Synchronized multi-view capture with aligned depth — 3D spatial structure and object tracking at every frame.
Contact forces at the end-effector. The signal almost no dataset has, and the one contact-rich policies need.
Full closed-loop internal robot states mapped to hardware-agnostic Cartesian spaces. Retargeting included.
Pre-labeled task IDs, object classes, and success/failure logs. Zero downstream cleaning required.
Access thousands of synchronized, real-world multimodal episodes tailored specifically to your token ingestion and model training specifications.
Ready-to-train datasets built to package directly into enterprise cloud infrastructure (such as AWS) for immediate client deployment.
Move past low-yield teleoperation to a continuous pipeline delivering multimodal episodes at a fractional marginal cost.
Feed your models the vital recovery loops needed to handle real-world chaos without collapsing.
Dataset outputs translate across completely different robot architectures — no embodiment-specific retraining.
Drastically reduce development and PoC deployment timelines, unlocking delayed ROI for Physical AI software and hardware.
Our second collection channel sends instrumented participants into their own homes — kitchens, bathrooms, laundry rooms — wearing RGB-D cameras and haptic gloves, performing everyday household tasks as they naturally would.
This captures the environmental chaos, behavioral variance, and physical interaction that a controlled environment can never replicate. The mess on the counter. The wet dish. The awkward cabinet angle.