Robotics training data. Recorded on real hardware, checked episode by episode.
No crawler can collect physical interaction. Every episode a robot policy learns from was recorded by a person, on a real arm, in a real room. eeko's data practice puts trained teleoperators, synchronized sensors, and expert outcome review behind each one, then binds consent and provenance to it before it reaches your training stack.
What is robotics training data?
Robotics training data is the recorded physical experience a robot learns from: demonstrations of a task performed through teleoperation or by a human hand, the video of it from several angles, the joint, gripper, and force readings that accompany it, and a label saying whether the attempt worked. Policies trained by imitation or fine-tuned from a robot foundation model are only as capable as the episodes behind them.
That creates a sourcing problem text models never had. There is no back catalogue of manipulation to download. Each episode has to be produced, on hardware, by someone who knows the protocol, in a scene varied enough that the policy learns the task rather than the room. Then the streams have to stay aligned, the outcome has to be labeled honestly, and anyone whose kitchen or workshop appears in the frame has to have agreed to it.
eeko's data practice handles the whole chain: operators, hardware access, recording protocol, review, releases, and delivery. It runs alongside eeko's manufacturing AI systems work, where the same policies end up on production lines. Start from the AI training data hub, or read how data capture sessions are run.
Teleoperation data and robot demonstration datasets, to your task list.
Six things robot learning teams ask for most. Every one starts with a written spec and a pilot batch you can load before volume.
- Task list and protocol fixed in the spec
- Operators drawn from eeko's teleoperation pool
- Camera placements agreed per task
- Sync verified on every episode
- Sample rates fixed in the spec
- Parquet or JSON Lines tables
- Second-expert decision on contested outcomes
- Label source recorded on each one
- Kitchens, workshops, warehouses, farms
- See video datasets
- Household and industrial object sets
- Annotated by 3D technicians
Where robot learning data usually goes wrong.
Simulation, then a few hundred episodes from the lab bench. The policy learns the lighting, the table, and the gripper it was shown. It has never seen a slip, so it has never seen a recovery. Nobody knows who was in the background of episode 212.
- Sim-to-real gap Real friction, glare, and clutter were never in the set
- Single embodiment Transfers poorly to the next platform
- Optimistic labels A mislabeled success teaches the wrong move
- Unreleased footage Faces and homes with no consent record
Recorded to a task distribution you wrote, across the grippers, platforms, and rooms you need to generalize over. Failures and recoveries at the ratio you chose. Every outcome label checked, every contested one decided by a second expert, every person in frame on a release.
- Real variation Homes, labs, and workplaces, not one bench
- Embodiment spread Several arms and grippers where that is the goal
- Reviewed labels Source-marked, second-expert on disputes
- Consent bound Releases and provenance on each episode
Trained operators and reviewers who know the hardware.
The people behind an episode
- Teleoperators — trained to a written protocol so that episode 900 looks like episode 9, with the rate stated before they accept a session.
- Robotics and mechanical engineers — define protocols, check stream alignment, and rule on contested outcomes. See mechanical engineering experts.
- Annotators and 3D technicians — sub-task boundaries, object tags, grasp labels, and scanned asset cleanup where the spec calls for them.
- Capture participants — tradespeople, cooks, warehouse staff, and others recording real work through eeko's paid capture programs, each on a release.
Every contributor is identity-verified before payment. Every label states its source, so a reviewer's call never gets confused with an operator's.
What an episode carries when it ships.
An illustrative episode lifecycle. The same checks run on every episode in the batch.
From task list to loaded episodes.
Fixed scope, fixed price once the spec is signed. Nothing is recorded at volume until you have trained on the pilot.
Robotics training data questions.
What is included in a robotics training dataset?
A typical eeko episode bundles the camera streams you asked for (wrist, head, third-person), the robot's joint positions and velocities, gripper commands, force-torque readings when the hardware exposes them, a task description, and a reviewed outcome label. Streams share one clock. The spec fixes the sample rates, camera placements, and the ratio of clean successes to failure-and-recovery episodes, because a policy learns a lot from watching a slip get corrected.
Can you record teleoperation data on our own robot?
Often, yes. During scoping we settle the embodiment, the task list, and the recording protocol. If your project needs one specific platform, we work out how operators get time on it and what that does to the schedule and the price. If generalization matters more than one arm, we record across several grippers and platforms instead. In both cases you see real episodes from the agreed setup in the pilot batch before volume starts.
How do you check the quality of robot demonstrations?
Every episode is checked for complete, time-aligned streams and an outcome label that matches what happened. Reviewers mark hesitations, collisions, and protocol breaks using the criteria written into your spec, and a second expert decides any contested outcome. Each label carries its source, operator, AI suggestion, or expert reviewer, so you can weight or filter episodes by label confidence when you train.
What formats is robotics data delivered in?
Episode tables as Parquet or JSON Lines, video as WebDataset shards, and Croissant metadata describing every field. 3D assets ship as STL, OBJ, PLY, or GLB, and point clouds as PLY. If your training stack expects a particular directory layout or field naming, we put it in the spec and the pilot batch arrives that way. Each delivery includes per-episode provenance records and a signed receipt.
How is privacy handled when recording in homes and workplaces?
Recording real tasks means recording real rooms and sometimes real people. A model release and a property release are bound to each episode, and an episode showing an identifiable person is released for training only when that person's consent is on record. Location data and personal metadata get stripped at intake, so none of it reaches you. The signed receipt gives your privacy team a per-episode record to review.
How much does robotics training data cost?
Price tracks operator time, hardware access, how many streams are synchronized, review depth, and licence scope. A long multi-step assembly episode costs more than a short pick-and-place. Exclusive rights cost more than shared rights, and standing orders or volume lower the unit price. We quote a fixed price for the pilot and for production once the spec is agreed, so you know the number before any recording starts.
Embodied AI training data in neighboring fields.
Solutions and experts robotics teams use most.
Send us the task your policy keeps dropping.
Describe the objects, the scenes, and the failure you are seeing. Inside one business day you hear from a data lead: questions, a sourcing outline, and a route to a pilot batch. No deck. A ranked list.
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