AI Robotics Data Annotation — 100× Faster Than Human Labelers.
KineTrace processes raw sensor data (MCAP, ROS bag, HDF5, RLDS) into training-ready labels with physics-layer detection. Under 2 minutes per 10-minute clip at $75–100, vs. $1,000–6,000 with human annotators.
Three Steps to Training-Ready Data
Upload
Drop in MCAP, ROS bag, HDF5, or RLDS files. KineTrace accepts all standard robotics data formats — no preprocessing, no conversion scripts needed.
Annotate
4D temporal-spatial analysis detects micro-slips, torque anomalies, and friction transitions invisible to human annotators — in under 2 minutes per 10-minute clip.
Export
Training-ready outputs directly to RT-2, Octo, OpenVLA, PyTorch, TensorFlow, or JAX. No conversion scripts.
KineTrace vs Human Annotation
| Capability | KineTrace | Scale AI / CVAT / Labelbox |
|---|---|---|
| Annotation method | Fully automated AI | Human labelers |
| Speed per 10-min clip | Under 2 minutes | Hours to days |
| Cost per 10-min clip | $75–100 | $1,000–6,000 |
| Physics-layer detection | Micro-slips, torque, friction | Not possible |
| Native robotics formats | MCAP, ROS bag, HDF5, RLDS | Limited / requires conversion |
| Export targets | RT-2, Octo, OpenVLA, PyTorch, TF, JAX | Generic labels only |
Works With What You Already Collect
Input Formats
MCAP, ROS bag, HDF5, RLDS. No preprocessing or conversion scripts required. Drop the raw file in and KineTrace handles the rest.
Physics-Layer Detection
4D temporal-spatial analysis finds micro-slips, torque anomalies, contact transitions, and friction changes that human annotators miss entirely.
Training-Ready Export
Direct export to RT-2, Octo, OpenVLA, PyTorch, TensorFlow, and JAX. No conversion work — plug in and train.
Ready to annotate your robotics dataset?
Tell us about your dataset — format, volume, and the robot learning task you're working on. We'll get back to you with a concrete estimate.