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Robots learn from people when a person demonstrates a task, pilots the robot remotely, or wears a camera that records first-person activity. A useful training episode links what the robot could observe with the action that followed, plus timing, robot state, task boundaries and outcome. After capture, the recordings may be synchronized, checked, labeled, stored, shared under a license, used for training or evaluation, and eventually deleted—but no universal retention or deletion rule applies to every project. The collection notice, consent or license, access controls, reuse terms and deletion process determine what happens next.
What “training data from people” actually contains
Robot-training data is more than a video file. A single episode can combine camera frames, robot joint positions, gripper state, velocity, force readings, control commands, timestamps, task instructions and the final outcome. The aim is to preserve the relationship between an observation and the action that followed it. Without that relationship, a clip of a hand moving does not tell a learning system which movement was a response to which visual cue.
Depending on the project, recordings can include external cameras, wrist-mounted cameras, audio, operator input, scene identifiers, task names and labels such as “success,” “failure,” object identity or action phase. Some datasets retain the raw streams; others retain transformed features, synchronized episodes or labels instead. The exact contents are set by the robot, sensors, task and consent or license terms.
Three ways people generate robot-learning data
Teleoperation: a person controls the robot
In teleoperation, an operator drives a simulated or physical robot while the system records the robot’s observations and the operator’s commands. RoboTurk describes a six-degree-of-freedom interface that maps phone movement to robot-arm movement, supports remote control of simulated and physical arms, and lets a person intervene when an autonomous robot needs help. That is one concrete research design, not a universal interface: other systems use joysticks, motion-tracking devices, keyboards, haptic controls or custom rigs.
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Teleoperation is valuable when the target robot’s action space matters. The demonstration already consists of commands the robot can execute, so a learning system can be trained on the same joint, end-effector or velocity representation it will use at run time. The trade-off is that the operator’s viewpoint and control latency can differ from those of an autonomous policy. Collection teams therefore record timing, state and sensor context so those differences can be measured and handled.
Direct human demonstration
A person can perform the task directly while cameras and other sensors observe it. The robot may later learn from the demonstrated sequence, even though the human’s body and tools do not share the robot’s exact joints. This approach can reveal useful task structure—how an object is grasped, where force is applied or which sub-step comes first—but the demonstrations must be mapped to the robot’s embodiment. A human hand trajectory is not automatically a valid robot-arm command.
First-person or egocentric recording
In an egocentric setup, a camera worn by a person records what that person sees while working. The resulting data can teach a system about viewpoint, hand-object interaction and the cues that precede an action. Commercial collection descriptions commonly distinguish direct human demonstrations, robot teleoperation and first-person recording; their service details apply to those providers’ own offerings rather than to every dataset.
Why synchronization and context decide whether a recording is useful
A learning episode needs a reliable timeline. Video frames, robot state and action commands should share timestamps or a known alignment. Teams may define an episode boundary at the start and end of a task, check that every sensor stream is complete, and attach metadata for the task, operator, scene and outcome. Optional labels can mark action phases, segmentation boundaries, success and the objects involved.
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Operant describes episodes recorded in a robot’s action space with episode boundaries, synchronized sensors and metadata for task, operator, scene and outcome. Its service description also lists optional segmentation, phase, success and object labels. Those features make a dataset easier to audit and filter, but they are not guaranteed steps for every provider.
Context is part of the learning signal. If an operator reaches for a cup after seeing a person point at it, the pointing gesture and the timing of the reach matter. Removing the gesture, the partner or the surrounding scene can leave a model with an incomplete explanation of why the action occurred.
A human-present example: the HABIT dataset
HABIT illustrates why people can be part of the task rather than merely data collectors. Its setup uses two Franka Research 3 arms and five synchronized RGB cameras, with cameras positioned on the robot side and the human side. Its taxonomy covers three arrangements:
- Collaborator: a human and robot act together.
- Coworker: the human and robot work separately in one shared space.
- Supervisor: a human directs the robot.
These roles expose coordination, shared-space and gesture-following situations that an empty-workspace recording cannot represent. The HABIT authors state: "Each operator acts only after directly observing the partner’s behavior. We prohibit any coordination signal that is not captured in the recorded observations, so every demonstrated action is grounded in cues that are also available to the policy." In practical terms, the cameras and other sensors must capture the cues that trigger an action; hidden instructions or unrecorded signals would give the policy information it will not have later.
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How a recording moves through a project
1. Capture and ingest
During a session, the system writes sensor streams and control data to storage. A project may also record participant identifiers, scene IDs, task instructions and consent status. File names, timestamps and device clocks are checked at ingest because missing or misaligned data can make an otherwise successful demonstration unusable.
2. Quality checks and synchronization
Teams can verify that cameras stayed connected, timestamps are monotonic, robot state is complete and the episode has a clear start and end. They may align streams, remove corrupted segments and flag occlusions or unsafe actions. These are common data-engineering practices, not a promise that every collection performs each check.
3. Labeling and metadata
Labels can identify the task, operator, scene, objects, action phases and outcome. A “success” label might mean that the intended object was placed correctly; another project could define success differently. Provenance—who collected the episode, with which hardware and under which protocol—helps users understand whether the data matches their robot and environment.
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4. Training and evaluation
Demonstrations can train a policy by pairing observations with actions, or they can be held out for evaluation. Human-present episodes can test whether a policy responds to a partner, works around a coworker or follows a supervisor’s gesture. A dataset may be used for both purposes, but mixing training and test episodes without separation can produce misleading scores.
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5. Sharing or licensing
A project may publish a dataset, provide access to approved researchers or license selected fields to customers. “Publicly described” does not mean “open for unrestricted reuse.” The license can limit commercial use, redistribution, derivative datasets, participant re-identification or combining the data with other sources. Raw video may have stricter access than anonymized labels or robot-state files.
6. Retention, decommissioning and deletion
After the active study, a team may archive, restrict, anonymize or delete files. ISO/CD 26264-1 describes a dataset lifecycle that reaches decommissioning, but it is a committee draft under development, not a final standard and not a universal retention schedule. A project’s notice and applicable law control the actual period and process.
Do robot companies keep videos of people?
Sometimes, but the title of the provider or the presence of a camera does not establish what is retained. A collection may keep raw video, retain only selected clips, convert footage into labels or features, or delete the raw stream after quality checks. It may also retain derived data—such as trajectories, embeddings or trained weights—even when the original video is no longer available.
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- Purpose: Is the recording used for a stated robot-training or evaluation task, safety analysis, product improvement or another purpose?
- Access: Which staff, contractors, researchers or customers can view raw footage or derived data?
- Sharing: Can the data be licensed, published, transferred across borders or combined with other datasets?
- Retention: How long are raw recordings, labels, logs and backups kept, and are different schedules used for each?
- Deletion: What channel can a participant use, what is actually deleted, and are legal or technical exceptions explained?
Privacy rules provide principles, not one robot-data timetable
The European Commission’s GDPR explainer says processing purposes must be known and people must be informed; a new use requires considering whether it is compatible with the original purpose. That is an official explanation of GDPR principles, not legal advice for a particular robot project. Consent and a notice therefore do not answer every downstream question by themselves.
For biometric and personal information in the United Kingdom, the Information Commissioner’s Office describes minimisation as keeping information adequate, relevant and necessary, and storage limitation as keeping it only as long as necessary. Its guidance page notes that it is under review following legislative changes, so time-sensitive compliance decisions should use the live guidance.
NIST SP 800-63A-4 discusses notice, retention and deletion processes for identity-proofing and biometric identity services. It is a useful governance example, but its scope is identity services; it does not set a rule for every robot-training dataset. Duties can also vary with jurisdiction, the type of data, the purpose, the people recorded and whether a shared space or bystanders are involved.
What happens to a trained model when a recording is deleted?
Deleting a source file does not automatically remove its influence from a model that has already been trained. The reviewed materials do not establish a general method for reversing, retraining or purging a particular recording’s contribution from a robot policy. A project may have a procedure for retraining or model replacement, but that procedure must be stated rather than assumed.
Ask whether deletion covers raw media only or also synchronized episodes, labels, backups, exported datasets, evaluation copies and model checkpoints. Also ask how a request is handled if a recording has already been licensed or combined with another dataset. The answer may be different for a participant’s identifiable image, a robot-state trajectory and an aggregate statistic.
Collection approaches compared
| Approach | How behavior is produced | Typical records | Main fit and limitation |
|---|---|---|---|
| Teleoperation | A person commands a simulated or physical robot. | Robot observations, state, control actions, timestamps and task outcome. | Strong match to the robot’s action space; operator viewpoint and latency can differ from autonomous use. |
| Direct demonstration | A person performs the task while sensors observe. | Video, optional audio, scene context, task steps and outcome labels. | Captures rich human technique; human motion must be mapped to the robot’s embodiment. |
| First-person recording | A person wears or carries a camera during activity. | Egocentric video, hand-object interactions, timing and contextual cues. | Shows a human viewpoint; privacy and bystander exposure require careful controls. |
| Human-present robot task | Human and robot collaborate, share space or follow supervision. | Synchronized human- and robot-side views, robot state, actions and interaction labels. | Teaches coordination behaviors; setup and annotation are more complex. |
No method is universally best. Compare task and embodiment match, sensor synchronization, outcome labeling, provenance, participant expectations, bystander exposure and the permitted reuse of the resulting data.
Concrete scale: what published projects have collected
RoboTurk’s dataset page reports 1,071 successful picking demonstrations, 1,147 successful assembly demonstrations and 3,224 demonstrations in total for a pilot associated with its CoRL 2018 work. Its real-world dataset page reports 111 hours of robot demonstrations collected in one week, with 54 non-expert users producing 2,144 demonstrations across three dexterous-manipulation tasks, in work associated with its IROS 2019 publication. These are historical, project-specific counts accessed in 2026—not an industry average for data volume, cost, retention or identifiability.
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A practical checklist before you participate or use a dataset
- Identify every sensor: external and wrist cameras, microphones, motion trackers, robot state and operator controls.
- Determine whether other people, faces, voices, screens, addresses or personal objects can enter the scene.
- Read the stated purpose and check whether evaluation, product improvement, licensing or publication is included.
- Separate raw recordings from labels, trajectories, embeddings, backups and trained models when asking about retention.
- Confirm who can access each data type and whether contractors, customers or researchers can receive it.
- Check geographic transfers, reuse limits and whether commercial or public redistribution is allowed.
- Find the deletion contact, deadline and exceptions, and ask what happens to copies already shared.
- If you are selecting data for a robot project, verify episode boundaries, clock alignment, action-space compatibility, outcome labels and provenance before training.
Common mistakes to avoid
- Calling every recording “video data.” Policies and models may depend on synchronized actions, robot state and metadata.
- Assuming teleoperation is the only way people teach robots. Direct and first-person demonstrations solve different data problems.
- Inferring a universal retention period from one provider. Storage and deletion are project- and jurisdiction-specific.
- Treating consent as a complete privacy answer. Purpose, access, sharing, retention and derived-data use still need explanation.
- Promising that deletion removes model influence. That requires a documented retraining or model-management process.
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