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Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space
2009
Advanced Robotics
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. In previous work, we proposed an approach based on Gaussian Mixture Regression (GMR) to find a controller for the robot reproducing the statistical characteristics of a movement in joint space and in task space through Lagrange optimization. In this paper, we
doi:10.1163/016918609x12529294461843
fatcat:ktodcepvd5bthe6giizkriie2e