Stochastic learning and control in multiple coordinate systems

A probabilistic interpretation of model predictive control is presented, enabling extensions to multiple coordinate systems. The resulting controller follows a minimal intervention principle, by learning and retrieving movements through the coordination of several frames of reference. When combined with a generative model, the approach can be used in various human-robot applications that are discussed in the paper.


Présenté à:
Intl Workshop on Human-Friendly Robotics, Genoa, Italy
Année
2016
Laboratoires:




 Notice créée le 2016-12-19, modifiée le 2018-09-13


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