Teleoperation for fidelity. UMI for real-world scale. Simulation for infinite, perfectly-labeled variation. We run all three — and blend them per task.
Trained operators drive your robot — or ours — through leader–follower rigs, VR, and exoskeletons, recording synchronized multi-view RGB-D, joint states, end-effector pose, gripper force, and tactile streams on one clock.
This is the gold standard for manipulation data: real dynamics, real contact, real sensor noise — exactly the distribution your policy will see at deployment.
Raw recordings aren't enough. Whatever the method, the output is the same: every angle on one clock, sliced into clean, labeled episodes you can train on directly.
Every camera and sensor is hardware-synchronized and frame-stamped, so vision, depth, force, and joint states all line up to the millisecond.
Continuous runs are segmented into individual task episodes with clear start and end points — the unit your policy actually trains on.
Each episode carries task metadata, operator ID, and scene context, so you always know exactly what you're training on and where it came from.
UMI is a handheld gripper with a wrist fisheye and onboard pose tracking — collectors demonstrate tasks anywhere, no robot on site. We recover 6-DoF trajectories with SLAM and retarget to your embodiment.
For multi-finger hands, DexUMI goes further: the collector wears a hand exoskeleton tuned to your robot hand, capturing dexterous motion with real tactile feedback, while a vision pipeline swaps in your robot's hand frame-by-frame. Published results report ~3× faster collection than teleoperation.
Built on Isaac Sim and MuJoCo with assets from our sim-to-real library, we generate photoreal, physics-validated episodes with domain randomization over lighting, texture, pose, and dynamics.
Ground truth comes free — masks, depth, 6-DoF poses, contact events — and unsafe or impossible edge cases are just another seed.
Tell us the task and the embodiment. We'll recommend a teleop / UMI / sim mix and ship a benchmark batch you can train on.