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.
Also known as: learning from demonstration, LfD, learning by demonstration
Updated
What is learned from an expert
Imitation learning uses demonstrated behaviour to learn how a robot should act. The supervision can be direct state-action examples, comparisons between behaviours or an objective inferred from demonstrations. The published survey An Algorithmic Perspective on Imitation Learning distinguishes direct replication, including behaviour cloning, from methods that infer the expert’s underlying objective.
For robots, a demonstration may come from teleoperation, kinesthetic teaching, an autonomous expert or retargeted human motion. A video alone does not contain the robot’s motor command, so human demonstrations need correspondence, action inference or retargeting before they can supervise low-level control.
Behaviour cloning is one approach
Behaviour cloning treats expert observation-action pairs as supervised training data. It is simple and widely used, but the learned policy can enter states absent from the expert dataset after making small errors. Interactive imitation methods collect supervision in states reached by the learner.
DAgger is a foundational example: it repeatedly runs the current policy, asks the expert for actions on visited states and adds those labelled states to the training set. This targets the mismatch between the expert’s state distribution and the learner’s own rollouts.
Data considerations for humanoids
Useful humanoid demonstrations align camera data, proprioception, contact signals and actions on one timeline. They also identify the robot, reference frames, action units and controller mode. Coverage of corrections, balance disturbances and failed attempts matters when recovery is part of the task. Demonstrations show what the collector attempted under particular conditions; they do not by themselves prove that a learned policy will be safe or reliable outside that distribution.
Sources
Related terms
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.
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.
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.
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.