A robot learning to pull a model train on its track

A Barrett WAM 7 DOFs manipulator learns to play a melody by pressing three big keys and learns to pull a model train on its track.

Kinesthetic teaching is used to acquire the skills from demonstrations. During reproduction, 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.

Without perturbation, both types of parameterization produce similar behavior. By temporary holding the robot hand, biasing the system to one type of constraints (temporal or spatial weight) produce different behaviors, by continuing the progress of the weights in the melody playing task, and by freezing the current weights in the model railway task. In the melody playing task, the path followed by the robot takes a shortcut to recover from the time delay induced by the perturbation. In the model railway task, the robot continues the motion without changing the shape of the path.

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