Abstract

Learning motor skills from multiple demonstrations
presents a number of challenges. One of those challenges
is the occurrence of occlusions and lack of sensor coverage,
which may corrupt part of the recorded data. Another issue
is the variability in speed of execution of the demonstrations,
which may require a way of finding the correspondence between
the time steps of the different demonstrations. In this paper,
an approach to learn motor skills is proposed that accounts
both for spatial and temporal variability of movements. This
approach, based on an Expectation-Maximization algorithm to
learn Probabilistic Movement Primitives, also allows for learning
motor skills from partially observed demonstrations, which may
result from occlusion or lack of sensor coverage. An application
of the algorithm proposed in this work lies in the field of
Human-Robot Interaction when the robot has to react to human
movements executed at different speeds. Experiments in which
a robotic arm receives a cup handed over by a human illustrate
this application. The capabilities of the algorithm in learning
and predicting movements are also evaluated in experiments
using a data set of letters and a data set of golf putting
movements.