2 minutes. Not 2 weeks. AI-powered 4D temporal-spatial annotation for multi-sensor robotics data — detecting physics events humans can't see.
Multi-sensor robot data ingest
Temporal-spatial + physics-layer analysis
Export for VLA, imitation & RL models
Robotics data annotation is the #1 bottleneck preventing faster model iteration. The old way is slow, expensive, and fundamentally blind.
Human annotators charge $100–600/hour. A single 10-minute multi-sensor clip costs thousands — and your team processes dozens per week.
Outsourced labeling creates multi-day delays. Your engineers sit idle waiting for annotations while competitors iterate faster.
Humans physically cannot detect micro-slips, torque anomalies, or sub-millimeter friction changes — the exact events that cause robot failures.
“We were spending more on annotation than on compute. Our robotics pipeline was bottlenecked by humans who couldn't even see the data that mattered most.”— ML Lead, Series B Robotics Company
It doesn't have to be this way.
An Agentic Convergent product. The 4D temporal-spatial AI annotation engine purpose-built for robotics. Three steps from raw sensor data to training-ready labels.
Drop your MCAP, RLDS, or ROS bag files. Aurevix natively ingests camera, LiDAR, force-torque, joint state, and tactile data simultaneously.
Our 4D temporal-spatial engine fuses all sensor modalities, detecting physics-layer events invisible to humans — micro-slips, torque anomalies, friction transitions.
Export training-ready annotations in seconds. Compatible with RT-2, Octo, OpenVLA, and all major imitation learning and RL frameworks.
Aurevix doesn't just do annotation better — it does annotation that's impossible any other way.
What takes a team of human annotators 20–40 hours per 10-minute clip, Aurevix completes in under 2 minutes. Your model iteration cycle goes from weeks to hours.
Slash annotation costs by 90%. At $75–100 per clip vs. $1K–6K with human labelers, you can annotate 10× more data for the same budget.
10× more data, same budgetAurevix detects physics events invisible to human annotators: micro-slips (sub-mm), torque anomalies, friction coefficient changes, and contact dynamics — the exact events that cause robot failures.
✦ Only Aurevix| Feature | Aurevix | Scale AI | CVAT | Labelbox |
|---|---|---|---|---|
| Multi-sensor fusion (camera+LiDAR+force-torque) | ✓ | ✗ | ✗ | ✗ |
| Physics-layer event detection | ✓ | ✗ | ✗ | ✗ |
| Native MCAP/RLDS/ROS bag support | ✓ | ✗ | ◐ | ✗ |
| 4D temporal-spatial annotation | ✓ | ✗ | ✗ | ✗ |
| Processing time per 10-min clip | <2 min | 2–5 days | 20–40 hrs | 2–5 days |
| Cost per 10-min clip | $75–100 | $3K–6K | $1K–2K | $2K–5K |
| Robot-specific domain models | ✓ | ◐ | ✗ | ◐ |
| VLA model export (RT-2, Octo, OpenVLA) | ✓ | ✗ | ✗ | ✗ |
Whether you're training humanoids, automating warehouses, or developing next-gen foundation models — Aurevix accelerates your pipeline.
Label grasping, tool use, and dexterous manipulation data for humanoid robot learning — capturing the micro-slip and contact events that define task success or failure.
Annotate high-throughput pick-and-place sequences with force-torque context. Detect near-misses and grip failures automatically.
Generate the training data that vision-language-action models need. Export directly to RT-2, Octo, and OpenVLA-compatible formats.
Automatically flag anomalous physics events in deployment logs. Identify failure precursors before they cause costly incidents.
Join the robotics teams already annotating 100× faster. Limited pilot slots available for qualified teams.