Sensor data, manipulation tasks and simulation annotation.
Overview
AI in Robotics
Robotics AI depends on richly annotated sensor data: depth maps, manipulation trajectories and simulation-to-real transfer datasets, to train perception and control systems that operate safely in the physical world.
Key challenges
- Multi-sensor annotation across depth, RGB and force-torque data
- Manipulation-task labeling requiring physical task understanding
- Sim-to-real gap requiring carefully matched simulation datasets
- Safety-critical accuracy requirements for physical interaction models
Our solutions
How we help
- 01Cross-sensor annotation pipelines with depth and RGB alignment
- 02Task-trajectory labeling by annotators trained on robotic manipulation
- 03Simulation dataset curation matched to real-world deployment conditions
- 04Safety-first QA with physical-plausibility validation on every batch
Results
92%Task-success model accuracy
1.5MSensor frames annotated
8 wksPipeline delivery
Power yourrobotics AI
Start with a scoped pilot: results in weeks, not months.