Definition and Composition of Motor Primitives Using Latent Force Models and Hidden Markov Models
نویسندگان
چکیده
Traditional methods for representing motor primitives have been purely data-driven or strongly mechanistic. In the former approach new movements are generated using existing movements and these methods are usually very flexible but their extrapolation capacity is limited by the available training data. On the other hand, strongly mechanistic models have a better generalization ability by relying on a physical description of the modeled system, however, it may be hard to fully describe a real system and the resulting differential equations are usually expensive to solve numerically. Therefore, in this work a different motor primitive parameterization is proposed using a hybrid model which jointly incorporates the flexibility of the data-driven paradigm and the extrapolation capacity of strongly mechanistic models, namely the Latent Force Model (LFM) framework.
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تاریخ انتشار 2016