Robotics Data Collection Glossary

A robotics data collection glossary defines the terms teams use when planning custom real-world capture for physical AI: teleoperation, sensor synchronization, metadata, QA gates, edge cases, and delivery packages. Use it to align operators, ML engineers, and buyers before scoping a pilot, so the collected data matches the robot, environment, and training objective.

Shared vocabulary matters because a robotics data request can sound simple while hiding decisions about embodiment, action space, sensors, privacy, and ownership. Operant uses these terms during robotics data collection scoping so every capture program is built to spec, not pulled from a marketplace or generic catalog.

Core capture terms

Real-world data collection

Real-world data collection means capturing sensor, action, and outcome data in physical environments that resemble deployment. For robotics teams, that usually includes the robot or rig, operators or policies, objects, fixtures, lighting, resets, failures, and safety constraints. The goal is not raw volume by itself; the goal is evidence that matches the conditions a model will face.

Custom capture

Custom capture is a data collection program designed around one team's robot, task, sensors, environment, and acceptance criteria. It differs from buying a public dataset because the scenario, data rights, metadata, and delivery format are defined before capture starts. Operant's work is custom capture for robotics and physical AI, not brokerage or resale of prebuilt files.

Embodiment

Embodiment describes the robot body and control surface that generate the data: arm geometry, gripper, mobile base, humanoid joints, cameras, action space, control frequency, and constraints. Embodiment mismatch is one reason generic data can fail in training. A policy trained on the wrong gripper, camera angle, or action representation may not transfer cleanly to the deployed robot.

Action space

Action space is the representation of commands available to the robot or operator. It may include joint positions, end-effector poses, base velocity, gripper state, torque commands, or higher-level skills. Collection should capture actions in the form your model or evaluator expects, or include a documented transform that downstream engineers trust.

Episode

An episode is one bounded attempt at a task. A useful episode has a start condition, goal, success or failure outcome, reset rule, and links to the sensor and action streams captured during the attempt. Episode boundaries are important for imitation learning, reinforcement learning from demonstrations, evaluation slices, and QA review.

Methods and operating modes

Teleoperation

Teleoperation means a human operator controls the robot through a remote or assisted interface while the system records observations, actions, timing, and outcomes. It is common when teams need expert demonstrations in the robot's action space. A teleoperation capture plan should define operator training, safety stops, control latency, reset procedures, and what counts as an accepted demonstration.

Human demonstration

Human demonstration captures a person performing the target behavior directly, often with egocentric cameras, motion capture, tactile signals, or object-state tracking. It can help define task structure before robot-ready teleoperation exists. The key question is how human movements will map to the robot embodiment and whether the captured observations are useful for the intended model.

Autonomous logging

Autonomous logging records the robot running a scripted routine, baseline policy, or production behavior. It is useful for finding failures, measuring regressions, and collecting evaluation examples. Autonomous logs need clear policy versioning, environment metadata, intervention markers, and outcome labels so teams can tell why each episode matters.

Edge case

An edge case is a rare or difficult condition that matters because it breaks a model, not because it is statistically common. Examples include reflective packaging, cluttered thresholds, partial grasps, occluded signs, wet pavement, unexpected humans, or worn fixtures. Edge cases should be created or captured safely and labeled with enough context to reproduce the condition.

Sensor and synchronization terms

Multimodal data

Multimodal data combines more than one signal: RGB, depth, LiDAR, IMU, force/torque, audio, proprioception, control commands, operator input, labels, and scene metadata. Multimodal capture is valuable only when the streams are aligned and documented. A pile of files is not the same thing as usable physical AI training data.

Sensor synchronization

Sensor synchronization is the process of making timestamps, calibration, and clock assumptions consistent across streams. Multi-camera RGB-D, LiDAR, IMU, proprioception, and control logs should line up closely enough for the model or evaluator to trust cross-sensor events. Operant treats multi-sensor synchronization as a deliverable, including calibration files and drift checks.

Calibration

Calibration describes the measurements that connect sensors to each other and to the robot or world frame. Camera intrinsics, extrinsics, depth alignment, LiDAR transforms, force offsets, and time offsets all affect whether an episode can be interpreted later. Calibration files should be versioned with the data they explain.

Metadata

Metadata is the structured context that makes captured files searchable and auditable. Useful metadata can include task name, robot version, sensor rig, operator or policy ID, environment, object set, lighting, weather, reset type, outcome, failure mode, consent status, and QA decision. Without metadata, teams may collect data they cannot confidently filter or reuse.

Quality and delivery terms

QA gate

A QA gate is a pass/fail check applied during or after capture. Common gates include sensor presence, timestamp alignment, calibration validity, episode boundaries, task visibility, label completeness, privacy exclusions, and delivery format. QA gates should be defined before volume scales so bad capture does not become a large cleanup project.

Acceptance criteria

Acceptance criteria are the rules for deciding which episodes are usable. They can cover technical quality, scenario coverage, safety, metadata completeness, and downstream file requirements. Strong criteria are specific enough that an operator, reviewer, and ML engineer would make the same decision about accepting or rejecting a sample.

Delivery package

A delivery package is what the customer receives after capture: synchronized logs, calibrated sensor files, labels, metadata, QA reports, documentation, and storage layout in the agreed format. The package should be easy for an engineer to load without reconstructing the capture operation from memory. The pilot scoping guide lists the decisions to settle before delivery.

Data ownership

Data ownership defines who controls the raw logs, processed files, labels, metadata, calibration artifacts, and derived deliverables from the engagement. In custom capture, ownership and permitted use should be documented before collection starts. Operant's FAQ on data ownership explains how this differs from public dataset licensing.

Planning terms buyers should align on

Target behavior

The target behavior is the skill, task, or failure mode the data should improve. It should be written in operational language: what the robot does, what success looks like, what failures count, which environment variables matter, and what model decision the data is meant to support.

Coverage

Coverage describes the range of conditions represented in the accepted data. It may include object types, layouts, weather, lighting, operators, robot states, failure modes, or geographies. Coverage is not the same as volume. A smaller set of well-labeled episodes can be more useful than a large set that misses the deployment conditions.

Pilot

A pilot is a small production-shaped capture run that validates the scenario, sensors, QA gates, delivery format, and scale plan. It should prove that the data can be collected safely and used by the downstream pipeline before a larger program starts. A pilot is most useful when it exercises the same process intended for production.

Sim-to-real gap

The sim-to-real gap is the difference between simulated or lab conditions and what the robot encounters in deployment. Real-world collection can close that gap by capturing the contact dynamics, timing, sensor noise, object variation, human interruption, and environmental mess that simulation or open datasets often miss.

How to use this glossary during scoping

Before a discovery call, mark which terms are already specified and which still need decisions. If the robot embodiment, sensor stack, QA gates, data rights, or delivery format are unclear, the capture plan is not ready to scale. If the target behavior, environment, and acceptance criteria are concrete, Operant can translate them into a custom real-world collection plan for your physical AI pipeline.