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Reference

Humanoid Robotics Glossary

Clear, source-backed definitions of humanoid robotics, embodied AI, robot learning, training data, control, and simulation terms.

A

Models & learning
Action chunking
Action chunking is a policy design in which one observation is used to predict a sequence of several future robot actions rather than a single next action. The chunk can capture short-term motion structure and reduce the effective number of sequential predictions, while its execution and replanning schedule determine how quickly the robot can react to new observations.
Models & learning
Asynchronous inference
Asynchronous inference is a deployment pattern that overlaps robot action execution with prediction of a later action chunk. A robot client consumes queued actions while a policy process or server evaluates a newer observation, then merges or replaces the remaining queue when the new chunk arrives. The pattern reduces idle inference gaps but does not remove latency, stale observations or safety constraints.

B

Models & learning
Behaviour cloning
Behaviour cloning is a form of imitation learning that fits a policy to expert observation–action pairs as a supervised prediction problem. For humanoid robots, the training examples typically align camera or proprioceptive observations with commands recorded during demonstrations, so the learned policy can reproduce similar behaviour without an explicit reward model.

D

Models & learning
Diffusion policy
A diffusion policy is a learned robot policy that generates an action or action sequence through iterative denoising conditioned on observations and, where applicable, a task goal. This formulation can represent several plausible behaviours for the same scene and is commonly trained by imitation on time-aligned trajectories of observations and expert actions.

F

Models & learning
Flow matching
Flow matching is a generative-model training method that learns a time-dependent vector field transporting samples from a simple prior distribution to a data distribution. Training regresses the vector field along chosen probability paths without solving the flow during each update; generation starts from the prior and numerically integrates the learned ordinary differential equation.

I

Models & learning
Imitation learning
Imitation learning is a family of methods that learns a policy from examples of expert behaviour rather than specifying every control rule by hand. In robotics, demonstrations pair observations or states with actions, trajectories or inferred objectives. Behaviour cloning is one imitation-learning method; interactive and inverse approaches address different supervision and distribution-shift problems.

M

Models & learning
Markov decision process
A Markov decision process is a mathematical model of sequential decision-making defined by states, actions, transition probabilities and rewards. After an agent chooses an action, the current state and action determine the distribution of the next state and reward. Reinforcement-learning methods use this structure to compare policies by expected cumulative reward.

P

Models & learning
Policy
A policy is the decision rule that maps a robot’s current observations or estimated state, and sometimes a task instruction, to an action or probability distribution over actions. It can be hand-designed or learned from demonstrations, rewards or both. In humanoid robotics, its outputs may be joint targets, torques, end-effector changes or higher-level skills.

R

V

Models & learning
Vision-language-action model
A vision-language-action model is a robot policy that conditions on visual observations and natural-language instructions to predict executable actions. It adapts a pretrained vision-language model, or a similar multimodal backbone, using robot trajectories so that semantic knowledge is connected to an embodiment’s action space. The action representation may use discrete tokens or continuous values.

W