Data for embodied intelligence
The complete package for robotics foundation models. Video, trajectories, and rich multimodal annotations — across every configuration, from pre-training to post-training to evals.
Full-stack robotics data
We cover every stage of the robotics ML pipeline.
Pre-training
Diverse environments, objects, and manipulation tasks at scale for building foundational robotics models.
Post-training
Expert teleoperation with precise action labels optimized for imitation learning.
Evals
Standardized tasks across manipulation and navigation for measuring real-world performance.
Data across configurations
Multiple camera perspectives and collection modalities.
Ego-centric
First-person camera mounted on the robot head or body. Captures the robot's perspective during task execution.
Overhead
External cameras positioned above the workspace. Bird's-eye view of the full scene and robot motion.
Ego-centric + Wrist
Combined head and wrist-mounted cameras. Full context plus close-up manipulation view for detailed grasping tasks.
Teleoperation
Human-controlled demonstrations with expert operators. High-quality trajectories optimized for imitation learning.
AI-powered diversity steering
Our robotics data engine automatically categorizes, validates, and steers collection to continuously improve diversity across every dimension that matters.
Ingest
Raw data streams in from collection sites worldwide
Categorize
AI auto-tags environment, objects, tasks, and operator style
QA + Validate
Automated quality checks and human review for edge cases
Steer
System identifies gaps and redirects collection to fill them
Environment coverage
Kitchens, offices, warehouses, retail spaces, and homes. Captured across varied lighting, clutter, and indoor–outdoor conditions.
Object diversity
Thousands of unique instances across materials (rigid, deformable, transparent), sizes, shapes, and weights.
Task variety
Pick and place, stacking, insertion, tool use, navigation, and multi-step sequential tasks.
Operator diversity
Multiple demonstrators per task with varied skill levels, styles, handedness, and demonstration speeds.
Robotics sample data
Explore sample robotics footage captured across tasks, sensors, and environments.
Human egocentric: Mono
Washing dishes
Human egocentric: Stereo
Groceries
Human egocentric: Trio
Folding clothes
Bimanual stationary
Stacking plates
Quad + Gripper
Sorting
NEXT-GEN ROBOTICS
During his visit to our SF robotics lab, Dwarkesh got a firsthand look at how we’re redefining robotics development with cutting-edge data collection.