Motor primitives are basic representations of human motion that, in a similar way to phonemes in a language, can be used to compose complex movements used for imitation learning in humanoid robotics. The first step when using motor primitives in imitation learning consists of defining a basic vocabulary of motor skills, according to a particular task that the humanoid robot is supposed to perform. Such vocabularies are usually learned from multivariate time course data.
In this talk, I will describe two alternatives for segmentation of motor primitives from multivariate time course data that involve the use of latent force models. A latent force model encodes a dynamic motor primitive in the form of a kernel function that can be used as the covariance function of a Gaussian process. I will describe how latent force models can be used on their own, or in combination with hidden Markov models for segmenting motion templates.
Dr. Álvarez received a degree in Electronics Engineering (B. Eng.) with Honours, from Universidad Nacional de Colombia in 2004, a master degree in Electrical Engineering (M. Eng.) from Universidad Tecnológica de Pereira, Colombia in 2006, and a Ph.D. degree in Computer Science from The University of Manchester, UK, in 2011. After finishing his Ph.D., Dr. Álvarez joined the Department of Electrical Engineering at Universidad Tecnológica de Pereira, Colombia, where he was appointed as a Faculty member until Dec 2016. From January 2017, Dr. Álvarez was appointed as Lecturer in Machine Learning at the Department of Computer Science of the University of Sheffield, UK.
Dr. Álvarez is interested in machine learning in general, its interplay with mathematics and statistics, and its applications. In particular, his research interests include probabilistic models, kernel methods, and stochastic processes. He works on the development of new approaches and the application of Machine Learning in areas that include applied neuroscience, systems biology, and humanoid robotics.