A Probabilistic Programming by Demonstration Framework Handling Constraints in Joint Space and Task Space

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.


Published in:
Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS)
Presented at:
IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS), Nice, France, September, 2008
Year:
2008
Keywords:
Note:
In press.
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 Record created 2008-06-06, last modified 2018-03-17

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