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Poster

Constraints measures and reproduction of style in robot imitation learning

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83791

Bakir,  GH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons85005

Ilg,  W
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84787

Giese,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Bakir, G., Ilg, W., Franz, M., & Giese, M. (2003). Constraints measures and reproduction of style in robot imitation learning. Poster presented at 6. Tübinger Wahrnehmungskonferenz (TWK 2003), Tübingen, Germany.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DD0C-6
Zusammenfassung
Imitation learning is frequently discussed as a method for generating complex behaviors in robots by imitating human actors. The kinematic and the dynamic properties of humans and robots are typically quite dierent, however. For this reason observed human trajectories cannot be directly transferred to robots, even if their geometry is humanoid. Instead the human trajectory must be approximated by trajectories that can be realized by the robot. During this approximation deviations from the human trajectory may arise that change the style of the executed movement. Alternatively, the style of the movement might be well reproduced, but the imitated trajectory might be suboptimal with respect to dierent constraint measures from robotics control, leading to non-robust behavior. Goal of the presented work is to quantify this trade-o between \imitation quality" and constraint compatibility for the imitation of complex writing movements. In our experiment, we used trajectory data from human writing movements (see the abstract of Ilg et al. in this volume). The human trajectories were mapped onto robot trajectories by minimizing an error measure that integrates constraints that are important for the imitation of movement style and a regularizing constraint that ensures smooth joint trajectories with low velocities. In a rst experiment, both the end-eector position and the shoulder angle of the robot were optimized in order to achieve good imitation together with accurate control of the end-eector position. In a second experiment only the end-eector trajectory was imitated whereas the motion of the elbow joint was determined using the optimal inverse kinematic solution for the robot. For both conditions dierent constraint measures (dexterity and relative jointlimit distances) and a measure for imitation quality were assessed. By controling the weight of the regularization term we can vary continuously between robot behavior optimizing imitation quality, and behavior minimizing joint velocities.