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- W2149936889 abstract "Imitation learning, also known as Programming by Demonstration, allows a non-expert user to teach complex skills to a robot. While so far researchers focused on abstracting kinematic relations, only little attention has been paid to force information. In this work we study imitation learning of human grasping skills from motion and force data. For this purpose a teleoperation system is realized that allows a human to control a simulated robotic hand and to grasp objects in a virtual environment. Haptic rendering algorithms are implemented to calculate interaction forces that occur when touching the virtual object. While learning of fingertip interaction forces is shown to result in physical inconsistency compared to the demonstrations, we show that learning of internal tensions leads to stable reproductions of the demonstrated grasping skill. Obtained results further indicate an enlarged generalisation capability of grasping skills learnt on the basis of motion and force data compared to grasping skills that encode kinematic relations only." @default.
- W2149936889 created "2016-06-24" @default.
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- W2149936889 date "2011-09-01" @default.
- W2149936889 modified "2023-10-18" @default.
- W2149936889 title "Imitation learning of human grasping skills from motion and force data" @default.
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- W2149936889 doi "https://doi.org/10.1109/iros.2011.6094951" @default.
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