### Abstract

We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. We propose an approach based on Gaussian Mixture Regression (GMR) to find automatically a controller for the robot reproducing the essential characteristics of the skill by handling simultaneously constraints in joint space and in task space. Experiments with two 5-DOFs Katana robots are then presented with two manipulation tasks consisting of handling and displacing a set of objects.

### Bibtex reference

@inproceedings{Calinon08IROS,
author="S. Calinon and A. Billard",
title="A Probabilistic Programming by Demonstration Framework Handling Skill Constraints in Joint Space and Task Space",
booktitle="Proc. {IEEE/RSJ} Intl Conf. on Intelligent Robots and Systems ({IROS})",
year="2008",
month="September",
location="Nice, France"
pages="367--372"
}

### Video

Incremental learning of a motion with two Katana robotic arms from Neuronics.
Two demonstrations are provided to show to the robots how to pour some liquid from a bottle into a glass. Then, the user moves manually one of the robot while the other robot follows as best as possible the learned motion in a flexible manner.

Video credit: Florent D'halluin.