Manipulation data can't be scraped from the web. We capture, label, and ship it — teleoperation, UMI, and simulation — aligned across every sensor and delivered in your training format.
Embodied AI doesn't fail on architecture. It fails on data.
The models are ready. What's scarce is high-fidelity, physically-grounded demonstration data — with the failures, the forces, and the recoveries left in.
That's the only thing we make.
Every task gets the right capture method — and often a blend of all three. Same rigor, same synchronization, same delivery.
Reward-dense golden trajectories from XR and ALOHA-style bimanual rigs, with full force-torque and proprioception on every frame.
Handheld gripper capture for real-world scale — hundreds of in-situ episodes an hour, no robot required, in the actual environment.
Photoreal episodes with domain randomization and free ground-truth labels — including the edge cases too unsafe or rare to stage for real.
Pixel-accurate segmentation, 6-DoF object poses, grasp points, and action phases — and every clip carries a causal chain-of-thought, structured for vision-language-action models.
Scene and robot assets in USD, URDF, and MJCF — domain-randomized, physics-validated, and benchmarked against real captures so the policies you train actually transfer to hardware.
Typed, validated, documented. We convert to the format your stack already speaks and match your conventions exactly — so the data drops straight into training with no glue code.
Teleop, UMI, and simulation — matched to your embodiment, reach, and task.
Segmentation, 6-DoF poses, grasps, action phases, and chain-of-thought.
Failure/recovery coverage, multi-stream sync checks, and human QA.
Delivered as RLDS, HDF5, MCAP, or LeRobot — in your schema.
We don't just record video. Vision, depth, force-torque, proprioception, end-effector trajectory, and language — hardware-timestamped to the same microsecond.
Vision tells you where things are. Force-torque, tactile arrays, and slip detection tell you what's happening at contact — the difference between crushing a berry and placing it. We capture all of it, on the same clock.
Ego and exo views of the full scene.
RealSense · 30 fps · 1080p
Metric geometry of objects and space.
stereo depth · LiDAR · registered
Contact, slip, and grasp quality.
6-axis F/T · 1 kHz · tactile skin
Joint angles, velocities, gripper state.
full kinematic state · per-frame
6-DoF pose and velocity of the hand.
position · orientation · velocity
Instructions plus causal rationale.
step labels · chain-of-thought
Policies don't learn robustness from clean demos. 15–25% of every corpus is the long tail — slips, mis-grasps, collisions, and the recovery that follows — labeled frame by frame so your model learns to fail gracefully and get back on task.
From domestic chaos to the factory line to the fulfillment center — the contact-rich, cluttered tasks where embodied AI actually has to work.
Deformable objects, clutter, and the messy long tail of the home — the tasks generalist humanoids have to master first.
Patient, careful interaction in the home — medication support, fetching and hand-off, mobility help. High-stakes tasks where gentleness and reliability are the entire point.
Whole-body, bimanual manipulation for general-purpose robots.
Precision assembly, kitting, and tool use on the line.
High-rate picking and induction in real fulfillment settings.
Not a generic labeling factory. We design capture around your robot's kinematics and sensor fusion — and prove it on a pilot.
Capture protocols built around your robot's DOF, reach, and sensor suite — not a one-size-fits-all labeling line.
15–25% of every corpus is contact-rich edge cases, recoveries, and near-misses — the data policies actually need.
RGB, depth, force-torque, and proprioception hardware-timestamped to ±1 ms. Verified, not assumed.
Expert teleoperation via XR and ALOHA-style bimanual rigs — smooth, intentional, reward-dense demonstrations.
Isaac Sim and MuJoCo assets with domain randomization, validated against real captures to close the reality gap.
RLDS, HDF5, MCAP, or LeRobot — typed, validated, and documented. Plug it straight into your stack.
Describe your robot and the behavior you're chasing. We'll propose a teleop / UMI / sim mix and deliver a benchmark batch you can train on — in your format.