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Acquiring and Generalizing the Embodiment Mapping From Human Observations to Robot Skills

Guilherme Maeda; Marco Ewerton; Dorothea Koert; Jan Peters
In: IEEE Robotics and Automation Letters (RA-L), Vol. 1, No. 2, Pages 784-791, IEEE, 2016.


Robot imitation based on observations of the human movement is a challenging problem as the structure of the human demonstrator and the robot learner are usually different. A movement that can be demonstrated well by a human may not be kinematically feasible for robot reproduction. A common approach to solve this kinematic mapping is to retarget predefined corresponding parts of the human and the robot kinematic structure. When such a correspondence is not available, manual scaling of the movement amplitude and the positioning of the demonstration in relation to the reference frame of the robot may be required. This letter's contribution is a method that eliminates both the need of human-robot structural associations-and therefore is less sensitive to the type of robot kinematics-and searches for the optimal location and adaptation of the human demonstration, such that the robot can accurately execute the optimized solution. The method defines a cost that quantifies the quality of the kinematic mapping and decreases it in conjunction with task-specific costs such as via-points and obstacles. We demonstrate the method experimentally where a real golf swing recorded via marker tracking is generalized to different speeds on the embodiment of a 7 degree-of-freedom (DoF) arm. In simulation, we compare solutions of robots with different kinematic structures.

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