Scalable Statistical Learning for Robotics
We are interested in supervised learning methods that accomplish nonlinear coordinate transformations and achieve robust internal models for our autonomous high-dimensional anthropomorphic systems.
Our focus is on the development of new learning algorithms for complex movement systems, where learning may proceed in an incremental fashion (i.e. sequential availability of data points). Using Bayesian approaches and graphical models, we aim to create algorithms that are fast, robust and based on a solid statistical foundation, yet scalable to extremely high dimensions.
Recent work has included the Bayesian Backfitting Relevance Vector Machine and a related variant (applied to EMG activity prediction). Both produce computationally efficient solutions and offer properties such as feature detection and automatic relevance determination. An augmented version of the algorithm's graphical model gives a Bayesian Factor Analysis regression model that performs noise cleanup. It offers significant improvements in generalization performance, as has been demonstrated in parameter identification tasks for our robotic platforms.
Contact persons: Jo-Anne Ting, Stefan Schaal
(:clmckeywordsearch statistical learning:)