Abstract
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.
Details
Title
Stochastic learning and control in multiple coordinate systems
Author(s)
Calinon, S.
Conference
Intl Workshop on Human-Friendly Robotics, Genoa, Italy
Date
2016
Laboratories
LIDIAP
Record Appears in
Scientific production and competences > STI - School of Engineering > IEM - Institut d'Electricité et de Microtechnique > LIDIAP - L'IDIAP Laboratory
Scientific production and competences > Euler Center for Signal Processing
Conference Papers
Work produced at EPFL
Scientific production and competences > Euler Center for Signal Processing
Conference Papers
Work produced at EPFL
Record creation date
2016-12-19