Movement Primitive Learning and Generalization : Using Mixture Density Networks
IEEE Robotics & Automation Magazine(2020)
摘要
Representing robot skills as movement primitives (MPs) that can be learned from human demonstration and adapted to new tasks and situations is a promising approach toward intuitive robot programming. To allow such adaptation, mapping between task parameters and MP parameters is needed, and different approaches have been proposed in the literature to learn such mapping. In human demonstrations, how...
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关键词
Task analysis,Trajectory,Entropy,Ground penetrating radar,Adaptation models,Robot motion
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