Encoding the time and space constraints of a task in explicit-duration Hidden Markov Model

We study the use of different weighting mechanisms in robot learning to represent a movement as a combination of linear systems. Kinesthetic teaching is used to acquire a skill from demonstrations which is then reproduced by the robot. The behaviors of the systems are analyzed when the robot faces perturbation introduced by the user physically interacting with the robot to momentarily stop the task. We propose the use of a Hidden Semi-Markov Model (HSMM) representation to encapsulate duration and position information in a robust manner with parameterization on the involvement of time and space constraints. The approach is tested in simulation and in two robot experiments, where a 7 DOFs manipulator is taught to play a melody by pressing three big keys and to pull a model train on its track.