¹Robots are used in human environments today more than ever before.

²Examples of such robots include simple vacuum cleaning or lawn mowing robots. ³Although robots have become increasingly popular, they are still mainly used in industrial settings as welders and assemblers. ⁴The reason for robots excelling in industrial settings is that the environment is for the most part static and that the task at hand is known a priori. ⁵For this reason, the robot can be preprogrammed to precisely follow specific trajectories.

⁷Thus, for a robot to learn in these situations, one possible and frequently used solution is to learn from a human demonstrator. ⁸The concept of having a human teach the robot by interacting with it is known as Learning from Demonstration (LfD), as well as Programming by Demonstration (PbD) or imitation learning. ⁹LfD has enabled robots to carry out complex tasks, such as playing the ball-in-a-cup game [50], playing ping pong [61], as well as many other tasks [10,63]. ¹⁰For robots to acquire a movement, they first and foremost must record the demonstrated movement by using either their internal sensors or an external monitoring system. ¹¹Then, the responsibility is shifted over to the robot, which in turn is expected to learn a reasonable representation of the movement from the recorded information. ¹²This learning is achieved through the guidance of a learning algorithm, which must be able to produce a representation of the movement as well as cope with perturbations without risking failure.

¹³LfD has attract ed significant attention in recent years, with the number of papers written on the subject serving as a significant indicator [5,60,64]. ¹⁴Several reasons have been identifi ed for this increased interest [3,53]. ¹⁵The first reason is the reduced learning time compared to traditional approaches that require a human to program the robot off-line. ¹⁶Another reason is that by demonstrating the movement to the robot, the user can also predict the behaviour in advance before the robot executes the learned skill, as this should follow the demonstrated movement quite accurately. ¹⁷This, in turn, increases safety and is exceptionally important when integrating robots into human environments. ¹⁸Finally, because the acquired movement is in line with the human workspace, critical parts of the movement can be modelled very accurately.

²²A method for improving a skill through practice has already been implemented in robotics and is known as Reinforcement Learning (RL), in which the robot tries to improve the learned movement by interacting with the environment and receiving responses in the form of rewards [48]. ²³Based on these rewards, the learned movement is adapted to increase future rewards. ²⁴This resembles the way that humans acquire a new skill: first, we imitate the skill performed by another person, and then we subsequently practise the skill in order to fine-tune the movement until we can master it.

²⁶To explore new trajectories, noise is typically added to the learned representation of the skill, resulting in a slightly new trajectory.

²⁸Consequently, various solutions have been propos ed for generating noise. ²⁹For example, in [49] and [50], the generated noise is dependent on the learned model.