# xumanoids > Meet xumanoids: humanoid robots envisioned as a new workforce alongside people. Explore source-backed definitions in our humanoid robots glossary. ## Definition: xumanoids In robotics: Humanoid robots that understand context, learn from experience, and work alongside people. More broadly: Machines in human form, built to expand our abilities and serve people with care. ## Topics Humanoid robots, Embodied AI, Teleoperation, Robotic manipulation, Vision-language-action models ## Guidance - Xumanoid is a coined term defined by this site, rather than an established robotics classification. - Prefer the homepage when citing the original xumanoids definition and the individual glossary URLs when citing other definitions. - Glossary entries distinguish established technical terms from coined language and include sources for technical claims. - Humanoid robot describes physical form. Embodied AI, teleoperation, robotic manipulation, and vision-language-action models describe distinct technologies or capabilities. - A robot's shape does not establish its autonomy, intelligence, safety, or readiness for work. Prefer the cited primary sources for claims about a specific system. ## Site - [Home](https://xumanoids.com/) - [Glossary](https://xumanoids.com/glossary): Source-backed definitions of humanoid robots, embodied AI, teleoperation, robotic manipulation, and vision-language-action models. - [Search](https://xumanoids.com/search) - [rss](https://xumanoids.com/feed.xml) - [Full glossary text](https://xumanoids.com/llms-full.txt) ## Glossary - [3D dynamic scene graph](https://xumanoids.com/glossary/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. - [3D Gaussian splatting](https://xumanoids.com/glossary/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. - [Action chunking](https://xumanoids.com/glossary/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. - [Action Chunking with Transformers](https://xumanoids.com/glossary/action-chunking-transformer): 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. - [Action tokenization](https://xumanoids.com/glossary/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. - [Active perception](https://xumanoids.com/glossary/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. - [Admittance control](https://xumanoids.com/glossary/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. - [Affordance](https://xumanoids.com/glossary/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. - [Backdrivability](https://xumanoids.com/glossary/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. - [Behavior cloning](https://xumanoids.com/glossary/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. - [Bipedal locomotion](https://xumanoids.com/glossary/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. - [Capture point](https://xumanoids.com/glossary/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. - [Catastrophic forgetting](https://xumanoids.com/glossary/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. - [Center of mass](https://xumanoids.com/glossary/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. - [Centroidal dynamics](https://xumanoids.com/glossary/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. - [Configuration space](https://xumanoids.com/glossary/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. - [Contact wrench cone](https://xumanoids.com/glossary/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. - [Contact-implicit optimization](https://xumanoids.com/glossary/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. - [Control barrier function](https://xumanoids.com/glossary/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. - [Cross-embodiment learning](https://xumanoids.com/glossary/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. - [Dataset aggregation](https://xumanoids.com/glossary/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. - [Degrees of freedom](https://xumanoids.com/glossary/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. - [Denavit-Hartenberg parameters](https://xumanoids.com/glossary/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. - [Differentiable simulation](https://xumanoids.com/glossary/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. - [Diffusion policy](https://xumanoids.com/glossary/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. - [Domain adaptation](https://xumanoids.com/glossary/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. - [Domain randomization](https://xumanoids.com/glossary/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. - [Dynamic movement primitive](https://xumanoids.com/glossary/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. - [Embodied AI](https://xumanoids.com/glossary/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. - [End effector](https://xumanoids.com/glossary/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. - [Euler angles](https://xumanoids.com/glossary/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. - [Event camera](https://xumanoids.com/glossary/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. - [Extended Kalman filter](https://xumanoids.com/glossary/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. - [Flow matching](https://xumanoids.com/glossary/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. - [Force closure](https://xumanoids.com/glossary/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. - [Force control](https://xumanoids.com/glossary/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. - [Form closure](https://xumanoids.com/glossary/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. - [Forward dynamics](https://xumanoids.com/glossary/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. - [Forward kinematics](https://xumanoids.com/glossary/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. - [Friction cone](https://xumanoids.com/glossary/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. - [Generalist robot policy](https://xumanoids.com/glossary/generalist-robot-policy): A generalist robot policy is a learned action-selection model designed to perform multiple tasks across a range of robot settings. Its generality depends on the tasks, observations, action interfaces, and robot bodies included in training and evaluation. - [Hierarchical reinforcement learning](https://xumanoids.com/glossary/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. - [Homogeneous transformation](https://xumanoids.com/glossary/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. - [Humanoid robot](https://xumanoids.com/glossary/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. - [Hybrid position-force control](https://xumanoids.com/glossary/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. - [Imitation learning](https://xumanoids.com/glossary/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. - [Impedance control](https://xumanoids.com/glossary/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. - [Inertial measurement unit](https://xumanoids.com/glossary/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. - [Inverse dynamics](https://xumanoids.com/glossary/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. - [Inverse kinematics](https://xumanoids.com/glossary/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. - [Jerk](https://xumanoids.com/glossary/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. - [Joint](https://xumanoids.com/glossary/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. - [Joint-space control](https://xumanoids.com/glossary/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. - [Kalman filter](https://xumanoids.com/glossary/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. - [Kinematic chain](https://xumanoids.com/glossary/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. - [Kinematic singularity](https://xumanoids.com/glossary/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. - [Language-conditioned policy](https://xumanoids.com/glossary/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. - [Lidar](https://xumanoids.com/glossary/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. - [Linear inverted pendulum model](https://xumanoids.com/glossary/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. - [Manipulability](https://xumanoids.com/glossary/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. - [Model predictive control](https://xumanoids.com/glossary/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. - [Motion planning](https://xumanoids.com/glossary/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. - [Motion retargeting](https://xumanoids.com/glossary/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. - [Null space](https://xumanoids.com/glossary/null-space): The null space of a matrix is the set of vectors it maps to zero. For a robot task Jacobian, it contains joint velocities that produce no instantaneous motion in the specified task coordinates. - [Occupancy grid](https://xumanoids.com/glossary/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. - [Odometry](https://xumanoids.com/glossary/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. - [Offline reinforcement learning](https://xumanoids.com/glossary/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. - [Operational-space control](https://xumanoids.com/glossary/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. - [Particle filter](https://xumanoids.com/glossary/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. - [PID control](https://xumanoids.com/glossary/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. - [Point cloud](https://xumanoids.com/glossary/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. - [Policy distillation](https://xumanoids.com/glossary/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. - [Pose estimation](https://xumanoids.com/glossary/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. - [Prismatic joint](https://xumanoids.com/glossary/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. - [Probabilistic roadmap](https://xumanoids.com/glossary/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. - [Proprioception](https://xumanoids.com/glossary/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. - [Quasi-direct drive](https://xumanoids.com/glossary/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. - [Quaternion](https://xumanoids.com/glossary/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. - [Rapidly-exploring random tree](https://xumanoids.com/glossary/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. - [Redundant manipulator](https://xumanoids.com/glossary/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. - [Reinforcement learning](https://xumanoids.com/glossary/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. - [Residual reinforcement learning](https://xumanoids.com/glossary/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. - [Revolute joint](https://xumanoids.com/glossary/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. - [Reward shaping](https://xumanoids.com/glossary/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. - [Rigid-body transformation](https://xumanoids.com/glossary/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. - [Robot foundation model](https://xumanoids.com/glossary/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 Jacobian](https://xumanoids.com/glossary/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. - [Robotic manipulation](https://xumanoids.com/glossary/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. - [Rotation matrix](https://xumanoids.com/glossary/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. - [Screw theory](https://xumanoids.com/glossary/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. - [Self-supervised learning](https://xumanoids.com/glossary/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. - [Sensor fusion](https://xumanoids.com/glossary/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. - [Series elastic actuator](https://xumanoids.com/glossary/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. - [Signed distance field](https://xumanoids.com/glossary/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. - [Sim-to-real transfer](https://xumanoids.com/glossary/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. - [Simultaneous localization and mapping](https://xumanoids.com/glossary/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. - [State estimation](https://xumanoids.com/glossary/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. - [Support polygon](https://xumanoids.com/glossary/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. - [Synthetic training data](https://xumanoids.com/glossary/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. - [System identification](https://xumanoids.com/glossary/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. - [Tactile sensing](https://xumanoids.com/glossary/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. - [Task and motion planning](https://xumanoids.com/glossary/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. - [Teleoperation](https://xumanoids.com/glossary/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. - [Test-time adaptation](https://xumanoids.com/glossary/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. - [Torque control](https://xumanoids.com/glossary/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. - [Trajectory optimization](https://xumanoids.com/glossary/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. - [Twist](https://xumanoids.com/glossary/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. - [Underactuation](https://xumanoids.com/glossary/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. - [URDF](https://xumanoids.com/glossary/unified-robot-description-format): 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. - [Vision-language model](https://xumanoids.com/glossary/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. - [Vision-language-action model](https://xumanoids.com/glossary/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. - [Visual servoing](https://xumanoids.com/glossary/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. - [Visual-inertial odometry](https://xumanoids.com/glossary/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. - [Whole-body control](https://xumanoids.com/glossary/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. - [Workspace](https://xumanoids.com/glossary/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. - [World model](https://xumanoids.com/glossary/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. - [Wrench](https://xumanoids.com/glossary/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. - [Xumanoid](https://xumanoids.com/glossary/xumanoid): Xumanoid is a coined term used on this site for a humanoid robot that understands context, learns from experience, and works alongside people. It describes an intended combination of humanlike form and collaborative behaviour, rather than an established technical class. - [Yaw](https://xumanoids.com/glossary/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. - [Young's modulus](https://xumanoids.com/glossary/youngs-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. - [Zero-moment point](https://xumanoids.com/glossary/zero-moment-point): The zero-moment point is a point on a chosen support plane where the net moment associated with the ground reaction wrench has zero components parallel to that plane. In flat-ground walking with the usual contact assumptions, it coincides with the center of pressure. ## Related sites - [robominder.ai](https://robominder.ai) - [humanoidsdata.com](https://humanoidsdata.com)