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.


Presented at:
Intl Workshop on Human-Friendly Robotics, Genoa, Italy
Year:
2016
Laboratories:




 Record created 2016-12-19, last modified 2018-01-28


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