glossary
Humanoid robots glossary
Source-backed definitions of humanoid robots, embodied AI, teleoperation, robotic manipulation, and vision-language-action models.
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Robotics
3D dynamic scene graph
A 3D dynamic scene graph is a layered graph that represents places, objects, people, and other spatial entities as nodes connected by geometric, semantic, and time-dependent relations. It gives a robot a structured scene representation above raw geometry alone.
Artificial intelligence
3D Gaussian splatting
3D Gaussian splatting is an explicit scene-representation and rendering method that models appearance with optimised three-dimensional Gaussian primitives. The primitives are projected and blended into an image, enabling novel-view rendering and, in some robotics systems, dense visual mapping.
A
Artificial intelligence
Action chunking
Action chunking is the prediction or organization of several future robot actions as one sequence. A policy can execute all or part of a chunk before using new observations to produce another sequence.
Artificial intelligence
Action Chunking with Transformers
Action Chunking with Transformers is an imitation-learning algorithm that predicts sequences of robot actions from observations using a transformer-based conditional variational autoencoder. It is usually abbreviated ACT.
Artificial intelligence
Action tokenization
Action tokenization converts robot actions or action sequences into discrete symbols that a model can predict and decode into control commands. The tokenizer defines how those symbols represent continuous or discrete robot actions.
Robotics
Active perception
Active perception is perception in which a robot chooses motions or interactions partly to obtain more useful observations. Examples include moving a camera to reveal an occluded object or touching an object to reduce uncertainty about its state.
Robot control
Admittance control
Admittance control converts measured or estimated interaction forces into a desired robot motion through a specified dynamic model. An inner motion controller then follows that reference.
Artificial intelligence
Affordance
An affordance is an action possibility offered by an environment to a particular agent. In robotics, the term often describes whether a robot can perform a specific action on an object or in a scene, sometimes represented by a learned score or spatial map.
B
Robotics
Backdrivability
Backdrivability is the ability of an external load applied at a mechanism’s output to drive motion back through its transmission. In a robot joint, it describes how readily an outside force can move the joint and its actuator.
Artificial intelligence
Behavior cloning
Behavior cloning is an imitation-learning method that trains a policy to predict a demonstrator’s actions from recorded observations or states. It treats action prediction as a supervised-learning problem.
Robotics
Bipedal locomotion
Bipedal locomotion is movement using two legs, with body motion coordinated through changing contacts between the feet and the environment. It includes walking and running.
C
Robot control
Capture point
The capture point is a model-dependent location where support can be placed to bring a moving robot toward rest without further steps. In the constant-height linear inverted pendulum model, the instantaneous capture point combines center-of-mass position and velocity.
Artificial intelligence
Catastrophic forgetting
Catastrophic forgetting is a substantial loss of previously learned capability when a model is trained on new tasks or data. It is a central problem in sequential and continual learning.
Robotics
Center of mass
The center of mass is the mass-weighted average position of a body or a collection of bodies. For an articulated robot, its position changes as the links move.
Robot control
Centroidal dynamics
Centroidal dynamics describe the motion of a multibody system’s center of mass and the evolution of its total linear and angular momentum. External forces and moments determine the rates of change of those momenta.
Robotics
Configuration space
Configuration space is the set of all possible configurations of a robot or mechanical system. Each point specifies the entire modeled arrangement, and the space has as many local dimensions as the system has degrees of freedom.
Robot control
Contact wrench cone
A contact wrench cone is the set of resultant forces and moments that a modelled contact can transmit without violating unilateral-contact and friction constraints. It gives legged-robot controllers a compact test for whether a foot or other support contact can remain feasible.
Robot control
Contact-implicit optimization
Contact-implicit optimization plans motion while allowing contact events and forces to emerge from contact constraints in the optimization. It avoids requiring every contact transition to be fixed in a predefined mode sequence.
Robot control
Control barrier function
A control barrier function is a mathematical function used to express a safe set for a dynamical system and constrain control inputs so the system remains inside that set. It is commonly used as a safety filter around a nominal robot controller.
Artificial intelligence
Cross-embodiment learning
Cross-embodiment learning uses experience from different robot bodies to train representations or policies that can transfer across those bodies. It requires a way to handle differences in sensing, geometry, and available actions.
D
Artificial intelligence
Dataset aggregation
Dataset aggregation, usually called DAgger in imitation learning, is an iterative algorithm that collects expert action labels at states visited by a learner. It adds those examples to an accumulated dataset and retrains the policy.
Robotics
Degrees of freedom
Degrees of freedom are the number of independent coordinates needed locally to describe a system configuration. In robotics, this count depends on the bodies, joints, and independent constraints in the model.
Robotics
Denavit-Hartenberg parameters
Denavit-Hartenberg parameters are four geometric quantities that describe the relative placement of successive link frames in a robot kinematic chain. They provide a systematic way to construct the transformations used in forward kinematics.
Artificial intelligence
Differentiable simulation
Differentiable simulation is physical simulation that provides derivatives of simulated outcomes or losses with respect to inputs such as controls, initial states, model parameters, or robot design variables. Those gradients can drive optimisation and learning through the simulated dynamics.
Artificial intelligence
Diffusion policy
A diffusion policy generates robot actions through a learned denoising process conditioned on observations. It commonly predicts an action sequence by progressively refining a noisy candidate rather than predicting one action with a single direct regression.
Artificial intelligence
Domain adaptation
Domain adaptation adjusts a learned model to work on a target data distribution that differs from its source training distribution. Robotics examples include adapting perception from simulation to camera images or adapting behavior to changed physical conditions.
Artificial intelligence
Domain randomization
Domain randomization varies properties of training environments to encourage a learned model or policy to work across changing conditions. In robotics it often randomizes simulated appearance, physical parameters, or both to support transfer to real hardware.
Robot control
Dynamic movement primitive
A dynamic movement primitive is a parameterised dynamical system that represents a goal-directed or rhythmic movement using stable baseline dynamics plus a learned shaping term. Robots can fit the parameters from demonstrations and adapt the resulting motion to a new goal or duration.
E
Artificial intelligence
Embodied AI
Embodied AI is artificial intelligence that perceives and acts through a body in a physical or simulated environment. It connects sensing, reasoning, and action rather than producing only text or images.
Robotics
End effector
An end effector is the part of a robot positioned to perform a task at the end of a manipulator, such as a gripper, hand, suction tool, or welding tool. Its pose and interaction forces are often the quantities a task controller regulates.
Robotics
Euler angles
Euler angles represent a three-dimensional orientation as an ordered sequence of three rotations about specified axes. Robotics often uses the term broadly to include roll-pitch-yaw conventions, so the exact rotation sequence must be specified.
Robotics
Event camera
An event camera is a vision sensor whose pixels asynchronously report changes in brightness instead of exposing complete image frames at fixed intervals. Each event normally carries a pixel location, timestamp, and change polarity.
Robot control
Extended Kalman filter
An extended Kalman filter is a state estimator that applies Kalman-style prediction and correction to nonlinear models by locally linearizing them. It approximates uncertainty around the current state estimate.
F
Artificial intelligence
Flow matching
Flow matching is a generative-model training method that learns a vector field for transforming a simple probability distribution into a data distribution. In robot learning, the generated samples can be continuous action sequences conditioned on observations and instructions.
Robotics
Force closure
Force closure is a contact condition in which the admissible contact wrenches can collectively oppose any external wrench direction on an object. The condition depends on contact locations, normals, and the assumed friction model.
Robot control
Force control
Force control regulates the force or wrench a robot applies to its environment. It may use a robot model, measured interaction forces, or both to produce joint commands that achieve a desired contact load.
Robotics
Form closure
Form closure is a condition in which the geometry of stationary contacts prevents an object from moving, without relying on friction. First-order form closure can be established from contact positions and normals alone.
Robot control
Forward dynamics
Forward dynamics predicts a robot's acceleration from its current configuration, velocity, applied joint forces or torques, and external forces. It uses the robot's mass, inertia, and other modeled dynamic properties.
Robotics
Forward kinematics
Forward kinematics calculates the position and orientation of a robot link or end-effector from the robot geometry and joint positions. It maps a robot configuration to a pose.
Robotics
Friction cone
A friction cone is the set of contact forces permitted by a Coulomb friction model at a contact that pushes but does not pull. The allowable tangential force magnitude is bounded by the normal force multiplied by a friction coefficient.
G
H
Artificial intelligence
Hierarchical reinforcement learning
Hierarchical reinforcement learning organizes learned decision-making into levels, often with a higher-level policy selecting goals or skills and lower-level policies producing actions. The levels can operate over different time scales.
Robotics
Homogeneous transformation
In rigid-body robotics, a homogeneous transformation is a 4-by-4 matrix that combines a three-dimensional rotation and translation. It represents a pose or changes coordinates between reference frames.
Robotics
Humanoid robot
A humanoid robot is a robot with a body arranged to resemble the human form, usually with a torso, arms, and legs. The term describes its physical form and does not by itself establish human-level intelligence or general autonomy.
Robot control
Hybrid position-force control
Hybrid position-force control regulates motion in some task directions and contact force in complementary constrained directions. It separates the commands according to the motion and force freedoms permitted by the environment.
I
Artificial intelligence
Imitation learning
Imitation learning learns behavior from examples supplied by a demonstrator. In robotics, demonstrations can teach a policy how to perform a task without requiring every action or objective to be programmed by hand.
Robot control
Impedance control
Impedance control shapes the dynamic relationship between a robot’s motion and the forces it exchanges with its environment. A common goal is for the robot to respond like a chosen mass, spring, and damper at a joint or end effector.
Robotics
Inertial measurement unit
An inertial measurement unit is a sensor assembly that typically combines accelerometers and gyroscopes to measure specific force and angular velocity. Some devices also provide magnetometer readings or estimated orientation.
Robot control
Inverse dynamics
Inverse dynamics calculates the joint forces or torques required for specified joint positions, velocities, and accelerations under a dynamics model. The result also depends on gravity and specified external loading.
Robot control
Inverse kinematics
Inverse kinematics finds joint positions that produce a desired robot end-effector position, orientation, or other geometric task. A target can have multiple solutions, no solution, or a continuous family of solutions.
J
Robot control
Jerk
Jerk is the rate at which acceleration changes with time, or the third time derivative of position. Robotics uses jerk limits to constrain how abruptly a commanded motion changes acceleration.
Robotics
Joint
A joint is a connection between robot links that constrains their permitted relative motion. Its kinematic type determines which rotations or translations the connected links can make relative to one another.
Robot control
Joint-space control
Joint-space control expresses a robot's motion targets and tracking errors in joint coordinates, such as joint angles or linear displacements. It regulates those coordinates rather than defining the primary motion error directly at the end-effector.
K
Robot control
Kalman filter
A Kalman filter is a recursive estimator that predicts a system's state with a linear model and corrects that prediction using noisy measurements. It tracks both the estimate and its error covariance.
Robotics
Kinematic chain
A kinematic chain is an arrangement of links connected by joints that constrains their relative motion. Open chains have no closed link loop, while closed chains contain at least one loop.
Robot control
Kinematic singularity
A kinematic singularity is a robot configuration where the task Jacobian has lower rank than the maximum it can attain for that mechanism and task. At that configuration the robot loses one or more instantaneous task-motion directions.
L
Artificial intelligence
Language-conditioned policy
A language-conditioned policy selects actions using a language instruction together with observations. The instruction specifies or modifies the behavior requested from the policy.
Robotics
Lidar
Lidar measures distance using emitted laser light and its return from surfaces. Repeated range measurements across directions can form a spatial scan or three-dimensional point cloud.
Robot control
Linear inverted pendulum model
The linear inverted pendulum model approximates a walking robot by a mass moving at constant height above its support, with simplified angular-momentum dynamics. These assumptions make horizontal center-of-mass acceleration linear in the displacement from the support point.
M
Robot control
Manipulability
Manipulability describes how a robot configuration maps joint motion into end-effector motion in different directions. It is commonly represented by a Jacobian-based velocity ellipsoid or summarized by a scalar measure.
Robot control
Model predictive control
Model predictive control repeatedly optimizes future actions using a system model, applies the next part of the solution, and replans from updated state information. It can account for objectives and constraints over a finite prediction horizon.
Robot control
Motion planning
Motion planning finds a robot movement from an initial state to a goal while satisfying constraints such as collision avoidance. A planner may produce a geometric path, a timed trajectory, or a sequence of controls.
Robotics
Motion retargeting
Motion retargeting maps a motion recorded or designed for one body onto another body with different geometry or joints. In robotics it produces a compatible pose or trajectory reference, which still needs a controller to execute it physically.
N
O
Robotics
Occupancy grid
An occupancy grid divides space into cells and records occupancy information for each cell. A two-dimensional robot map commonly distinguishes occupied, free, and unknown regions.
Robotics
Odometry
Odometry estimates changes in a robot's position and orientation from motion measurements over time. Its accumulated pose provides a local reference that can drift as measurement errors build up.
Artificial intelligence
Offline reinforcement learning
Offline reinforcement learning learns a reward-optimizing policy from previously collected experience without gathering new environment interactions during that learning stage. The data may come from earlier policies, demonstrations, or other collection procedures.
Robot control
Operational-space control
Operational-space control formulates a robot’s motion and force behavior in task coordinates, such as the position and orientation of its hand, while accounting for the robot’s dynamics. Secondary joint objectives can be coordinated with the primary task.
P
Robot control
Particle filter
A particle filter represents a probability distribution over possible states with a collection of weighted samples. It updates those samples using a motion model and new observations to estimate a changing state.
Robot control
PID control
PID control is feedback control that combines terms proportional to the current error, the accumulated error, and the rate of change of error. These terms determine the command sent to the controlled system.
Robotics
Point cloud
A point cloud is a collection of points representing sampled locations in space, usually with three-dimensional coordinates. Individual points may also carry attributes such as color or return intensity.
Artificial intelligence
Policy distillation
Policy distillation trains a student policy to reproduce behavior from one or more teacher policies. It can transfer learned behavior into a smaller network or combine multiple task-specific policies into one model.
Robotics
Pose estimation
Pose estimation determines the position and orientation of an object or robot relative to a reference frame. For a rigid body in three-dimensional space, a full pose has three translational and three rotational degrees of freedom.
Robotics
Prismatic joint
A prismatic joint permits one link to translate relative to another along a fixed joint axis without relative rotation. Its single degree of freedom is described by a linear displacement.
Robot control
Probabilistic roadmap
A probabilistic roadmap is a motion-planning graph built by sampling collision-free configurations and connecting nearby samples with feasible local paths. The graph can then answer start-to-goal queries within the modeled environment.
Robotics
Proprioception
Proprioception in robotics is sensing the robot's own motion, configuration, and internal physical state. Typical proprioceptive inputs include joint encoders, inertial measurements, and signals associated with actuator effort or contact.
Q
Robotics
Quasi-direct drive
Quasi-direct drive is an actuation approach that combines a torque-capable motor with a relatively low transmission reduction to preserve useful backdrivability and force-control behavior. It differs from direct drive because it still uses a transmission.
Robotics
Quaternion
A quaternion is a four-component mathematical object consisting of a scalar and a three-component vector. Robotics commonly uses unit quaternions to represent three-dimensional rotations without the coordinate singularities of Euler angles.
R
Robot control
Rapidly-exploring random tree
A rapidly-exploring random tree is a sampling-based structure that grows through a configuration or state space toward sampled targets. Motion planners use it to search for feasible routes through spaces with obstacles and movement constraints.
Robot control
Redundant manipulator
A redundant manipulator has more independent joint-motion variables than are needed for its specified end-effector task. This can allow different joint motions or postures to produce the same task result.
Artificial intelligence
Reinforcement learning
Reinforcement learning trains an agent to choose actions that maximize expected cumulative reward through experience with an environment. In robotics, the learned policy can select movements or higher-level behaviors from observations.
Artificial intelligence
Residual reinforcement learning
Residual reinforcement learning learns a corrective control signal that is combined with a baseline controller. The baseline handles part of the task while the learned residual adjusts behavior that is difficult to model or tune directly.
Robotics
Revolute joint
A revolute joint permits one link to rotate relative to another about a fixed joint axis. Its single relative degree of freedom is described by an angle.
Artificial intelligence
Reward shaping
Reward shaping adds supplementary rewards to guide reinforcement learning toward useful behavior. Poorly chosen shaping can change which policy is optimal, so an easier training signal is not automatically equivalent to the original task objective.
Robotics
Rigid-body transformation
A rigid-body transformation changes a body's position and orientation without changing its shape or size. In three-dimensional robotics it consists of a proper rotation and a translation.
Artificial intelligence
Robot foundation model
A robot foundation model is a model pretrained on broad data to support adaptation to multiple robot tasks, environments, or bodies. The term describes a reusable learning base rather than a guarantee of general physical competence.
Robot control
Robot Jacobian
A robot Jacobian is a configuration-dependent matrix that maps joint velocities to a chosen task velocity, often an end-effector twist. It describes the local relationship between joint motion and task motion.
Robotics
Robotic manipulation
Robotic manipulation is the use of a robot to change an object's position, orientation, or state through physical interaction. It includes grasping and moving objects as well as actions such as pushing or carrying them without a grasp.
Robotics
Rotation matrix
A rotation matrix represents an orientation or rotation while preserving lengths and angles. In three dimensions it is a 3-by-3 orthonormal matrix with determinant positive one.
S
Robotics
Screw theory
Screw theory is a geometric framework for describing rigid-body motion and forces using axes, rotation, translation, and pitch. In robotics it provides the basis for twist and wrench representations and screw-axis formulations of kinematics.
Artificial intelligence
Self-supervised learning
Self-supervised learning builds a training signal from the structure of the data itself rather than requiring a human label for every example. In robotics it can learn useful visual or temporal representations before a downstream task policy is trained.
Robot control
Sensor fusion
Sensor fusion combines information from multiple sensors or estimation sources to produce a shared estimate. The combination must account for coordinate frames, timing, uncertainty, and dependence between inputs.
Robotics
Series elastic actuator
A series elastic actuator places an elastic element in the force-transmission path between the drive and its load. Measuring the element’s deflection can support force or torque feedback while the elasticity changes the actuator’s response to impacts.
Robotics
Signed distance field
A signed distance field represents a surface by assigning spatial locations a distance value whose sign distinguishes the two sides of the surface. Its zero level marks the surface, while the sign convention depends on the representation.
Artificial intelligence
Sim-to-real transfer
Sim-to-real transfer applies a model, policy, or behavior developed in simulation to a physical system. Its central challenge is the difference between the simulated environment and the robot, sensors, and interactions encountered in reality.
Robotics
Simultaneous localization and mapping
Simultaneous localization and mapping is the joint estimation of a robot's state and a map of its environment from sensor observations. It is commonly abbreviated SLAM.
Robot control
State estimation
State estimation infers quantities describing a robot or its environment from measurements and a model. A robot state may include position, orientation, velocity, and other variables that are not all directly measured.
Robotics
Support polygon
The support polygon is the convex hull of a robot’s active contact points or contact patches projected onto a common support plane. It describes the available support region in planar contact models.
Artificial intelligence
Synthetic training data
Synthetic training data is data produced computationally for model training rather than collected directly as the corresponding real-world examples. In robotics, it often includes rendered sensor observations, simulated trajectories, and labels available from the simulator.
Robot control
System identification
System identification estimates a model of a physical system from measured inputs and outputs. In robotics, it can recover parameters such as inertia and friction or learn a more general model of how actions change the system state.
T
Robotics
Tactile sensing
Tactile sensing measures information arising from physical contact, such as contact geometry, deformation, or force. Robots use it to observe interactions at their fingers, grippers, feet, or other contact surfaces.
Artificial intelligence
Task and motion planning
Task and motion planning jointly searches over discrete task decisions and continuous robot motions. It connects choices such as which object to move or which grasp to use with geometrically and kinematically feasible trajectories.
Robot control
Teleoperation
Teleoperation is the control of a robot by a human operator from a separate location or interface. The operator supplies commands while feedback, such as camera images or the robot's motion, helps them guide the task.
Artificial intelligence
Test-time adaptation
Test-time adaptation adjusts a trained model using data encountered during evaluation or deployment. Unlike ordinary fixed-model inference, it updates model parameters or statistics in response to the target data.
Robot control
Torque control
Torque control regulates the turning effort delivered by an actuator or robot joint. It provides an actuation interface from which motion, force, and impedance controllers can produce the joint torques their tasks require.
Robot control
Trajectory optimization
Trajectory optimization finds a time-varying motion, and often control inputs, that minimizes an objective while satisfying specified constraints. Robot applications can include geometric, kinematic, and dynamic constraints.
Robot control
Twist
A twist is a six-component representation of a rigid body's instantaneous motion, combining angular and linear velocity. Its numerical values depend on the reference frame and the point used for the linear component.
U
Robotics
Underactuation
Underactuation means that a system’s available control inputs cannot independently command acceleration in every degree of freedom of its model. It often occurs when a mechanism has fewer independent actuators than degrees of freedom.
Robotics
URDF
URDF is an XML format for describing a robot's links, joints, geometry, and associated physical properties. ROS tools use it to represent a robot model for visualization, kinematics, and related applications.
V
Artificial intelligence
Vision-language model
A vision-language model processes visual information and natural language in a shared system. Depending on its design, it may connect images with text representations or generate text from visual and textual inputs.
Artificial intelligence
Vision-language-action model
A vision-language-action model is an AI model that uses visual observations and language instructions to produce actions for a robot. It connects what a robot sees and what it is asked to do with outputs that a robot controller can execute.
Robot control
Visual servoing
Visual servoing uses visual measurements inside a feedback loop to control robot motion. The controller updates movement to reduce an error defined from image features or visually estimated pose.
Robotics
Visual-inertial odometry
Visual-inertial odometry estimates a moving system's motion by combining camera observations with inertial measurements. It typically estimates position, orientation, velocity, and sensor biases over time.
W
Robot control
Whole-body control
Whole-body control coordinates a robot’s joints and contacts to satisfy several motion and force objectives together. In humanoids, it commonly combines balance, foot motion, hand tasks, and posture subject to physical constraints.
Robotics
Workspace
A robot workspace is the set of positions or poses its end-effector can reach under specified geometric and joint constraints. Its meaning depends on whether orientation is included and which base and tool configuration are assumed.
Artificial intelligence
World model
A world model is an internal predictive model of an environment and how it changes. In robot learning, it can predict future states or observations under possible actions to support planning or policy training.
Robot control
Wrench
A wrench is a six-component representation of force and moment acting on a rigid body. It combines three force components with three moment components about a specified reference point.
X
Y
Robotics
Yaw
Yaw is the rotation angle about the z-axis in a specified roll-pitch-yaw convention. For a level robot in a z-up frame, it describes heading in the horizontal plane.
Robotics
Young's modulus
Young's modulus is a measure of material stiffness equal to axial stress divided by axial strain in the linear elastic regime. It describes resistance to elastic stretching or compression, rather than the load at which a part fails.