End-to-End Robotics Data Pipeline
From egocentric capture to train-ready datasets — one API, every modality.
Every Sensor, Every Modality
Robots generate diverse data. We label all of it with temporal consistency across modalities.
Egocentric Capture
First-person, dual synced camera capture from the operator's point of view — the demonstration data vision-language-action models train on.
Vision
Multi-camera RGB video, stereo pairs, depth cameras — frame-by-frame annotation with temporal tracking.
3D Perception
LiDAR point clouds, RGBD sensors, 3D bounding boxes with full spatial understanding.
Proprioception
Joint positions, velocities, torques — the internal state data that makes robot learning work.
Force & Tactile
Force-torque sensors, tactile feedback arrays — critical for manipulation and dexterous grasping.
Audio
Environmental sound, voice commands, acoustic feedback — for human-robot interaction labeling.
Synchronized
All modalities temporally aligned with precise timestamps. One timeline, every sensor.
How Roboloop Works
Upload
Push your multi-camera video, LiDAR scans, and sensor streams via our API or dashboard.
→Annotate
Our robotics-trained annotators label 3D bounding boxes, object tracks, grasp points, and trajectories.
→Verify
Multi-pass human QA + LLM-assisted validation ensures temporal consistency and accuracy.
→Deliver
Receive training-ready datasets with confidence scores, metadata, and full provenance.
Human + AI Quality Control
Every label goes through our dual-layer verification system.
Multi-Pass Human QA
Every annotation is reviewed by a second specialist. Complex labels get third-pass review. Our QA team understands robotics failure modes.
LLM-Assisted Validation
AI judges check temporal consistency, spatial coherence, and label completeness automatically — catching errors humans might miss.