Reference
Humanoid Robotics Glossary
Clear, source-backed definitions of humanoid robotics, embodied AI, robot learning, training data, control, and simulation terms.
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Data & collection
Data synchronisation
Data synchronisation is the process of placing sensor, state, action, annotation, and outcome records on a common timeline so samples that describe the same physical instant or transition can be matched. It requires trustworthy timestamps or trigger relationships and an explicit rule for handling streams with different rates, delays, dropped samples, and clock offsets.
Data & collection
Demonstration
A demonstration is a recorded example of how an intended task or behaviour is performed, usually represented as a time-aligned sequence of observations, states and actions. For humanoid robot learning, demonstrations may come from teleoperation, kinaesthetic guidance, motion capture or autonomous experts and provide targets for imitation.
Data & collection
Depth data
Depth data records the distance associated with image locations or sensor rays, usually as a depth image in which each pixel stores a metric value relative to a camera. The exact geometry, units, invalid-value convention, and coordinate frame depend on the sensor and encoding. RGB-D data pairs depth with colour imagery; a point cloud is a separate 3D representation derived from or aligned with such measurements.
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Data & collection
Human–object interaction
Human–object interaction is the physical and semantic relationship between a person and an object while the person observes, reaches, grasps, moves, uses, or otherwise acts on it. In robotics datasets, the term often refers to recordings and annotations that connect human body or hand motion with object identity, pose, contact, action, and task context.
Data & collection
Human–robot interaction
Human–robot interaction is the exchange of information and actions between people and robots while performing a task, and the field that studies how such systems are designed and evaluated. It includes remote operation, supervision, collaboration, physical interaction and social communication rather than only face-to-face conversation.
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Data & collection
Language annotation
A language annotation is natural-language metadata attached to a robot-data sample, segment, or episode. It may state the instruction given before execution, describe what happened afterward, name a task or subtask, identify objects, record a correction, or explain an outcome. These annotation types are not interchangeable because they contain different information and may be available at different times.
Data & collection
LeRobot dataset
A LeRobot dataset is a robot-learning dataset organised for the LeRobot data model and loaders. LeRobot v3 stores low-dimensional state, action and timestamp fields in Parquet, camera streams in MP4, and schema, task, statistics and episode metadata under a coordinated directory layout.
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Data & collection
MCAP
MCAP is an open container format for timestamped robotics and multimodal log messages. It can store heterogeneous serialised messages with schemas, channels, attachments, metadata, optional compression, and indexes, but it does not define whether the recorded sensors, clocks, or robot actions are correct.
Data & collection
Motion capture
Motion capture is the measurement and reconstruction of a person’s or object’s movement over time, commonly as joint positions, orientations or a fitted body model. Optical markers, cameras and inertial sensors can supply the measurements. Humanoid robotics uses the resulting motion sequences for analysis, imitation and retargeting to a robot body.
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Data & collection
Robot state
Robot state is the set of variables used to describe a robot at a particular time, such as joint positions and velocities, base pose, end-effector pose, gripper state, actuator measurements, or estimated motion. In control theory, a complete state contains enough information to predict future evolution given an action; in robot datasets, “state” often means only the measured or estimated subset that was logged.
Data & collection
Robot training data
Robot training data is recorded experience used to train, fine-tune, or adapt models for robot perception, prediction, planning, or control. It can include sensor observations, robot state, actions, task instructions, rewards or outcomes, demonstrations, failures, and embodiment metadata. Not every dataset contains every field, but their timing and physical meaning must be clear.
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Data & collection
Teleoperation
Teleoperation is real-time human control of a robot from a remote or mediated interface. For humanoid training-data collection, the operator’s inputs are mapped to robot motions while cameras, proprioception, commands and outcomes are recorded, producing embodied demonstrations in the robot’s own observation and action spaces.
Data & collection
Trajectory
A trajectory is a time-ordered sequence of states or observations, actions and, where applicable, rewards generated as an agent or robot evolves. A complete episode or policy rollout often yields a trajectory, but the terms are not universally identical: trajectories may be partial, while episodes have dataset- or environment-defined boundaries.