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Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations

Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters, "Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations", IEEE Robotics and Automation Letters (RA-L), 2018.

Abstract

Most learning from demonstration approaches still require too many demonstrations to learn a skill or fail to generalize it to new situations. In this paper, we introduce Mixture of Attractors, a novel movement primitive representation, which allows for learning complex skills from very few demonstrations. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen object positions and orientations. In contrast to most other approaches, neither a heuristic nor a good initialization of parameters is needed to choose the coordinate frames. Instead, a skill is learned by solving a convex optimization problem. The system learns object-directed movements of arbitrary shape and automatically blends smoothly between successive movements. The approach is evaluated and compared to other movement primitive representations on data from the Omniglot handwriting data set and on real demonstrations of a handwriting task. The evaluations show that the presented approach outperforms other state-of-the-art concepts in terms of generalization capabilities and accuracy.



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