labelrobotics.io delivers proven datasets, annotations, and outcomes to robotics labs and physical AI companies. Every dataset your robots need — labeled, structured, and ready to train.
Outcomes delivered with world-class data, annotations, and infrastructure — across tactile sensing, force control, visual perception, and motion planning.
Annotate touch values, surface compliance, material texture, and grip contact with tooling built specifically for robotic manipulation. Every sensor frame labeled by domain experts — normalized, validated, and model-ready. labelrobotics.io's tactile datasets give your models the sense of touch they need to interact with the real world.
Explore Tactile Datasets →Labeled torque, joint angles, velocity vectors, and applied force — synchronized across all sensor streams with sub-millisecond temporal alignment. Our force datasets cover pick-and-place, manipulation, locomotion, and inspection tasks across humanoid, quadruped, and robotic arm embodiments. Adapt these datasets to your specific hardware in days, not months.
Explore Force Datasets →
labelrobotics.io's Data Engine powers the most advanced physical AI models in the world through world-class tactile annotation, force labeling, motion capture, and safety evaluation. The same flywheel that made LLMs powerful — built for robotics.
Unlike LLMs that train on text, robots need a completely different data vocabulary. Our schema captures the full physical interaction layer — usable by any company, any robot, any task.
| Column | Type | Description |
|---|---|---|
| touch_value | float32 | Normalized tactile contact intensity [0–1] |
| feel_value | float32 | Surface texture perception score [0–1] |
| grip_strength | float32 | Applied grip force in Newtons |
| torque_nm | float32 | Joint torque in Newton-meters |
| surface_compliance | enum | Rigid / Elastic / Soft / Fluid |
| temperature_c | float32 | Object surface temperature in Celsius |
| material_class | string | Metal / Plastic / Fabric / Organic / Glass |
| joint_angle_deg | float32[] | Per-joint angles in degrees (array) |
| velocity_ms | float32 | End-effector velocity in m/s |
| pressure_kpa | float32 | Contact pressure in kilopascals |
| slip_detected | bool | Whether grip slip was detected |
| vibration_hz | float32 | Surface vibration frequency in Hz |
| object_weight_g | float32 | Estimated object mass in grams |
| task_success | bool | Whether the robot task completed successfully |
labelrobotics.io's annotated datasets are available to any physical AI team — from early-stage robotics startups to large-scale manufacturers. Plug our labeled data directly into your training pipeline and start training in hours, not months.
Full-body manipulation, locomotion, and dexterous grasping datasets
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