Abstract

I propose a set of Bayesian methods to help us work towards the goal of autonomous real-time learning. Specifically, I am interested in scenarios where the input data has thousands of dimensions and where real-time, incremental learning may be needed, as in robotics, real-time vision, brain-computer interfaces, autonomous vehicles etc. Real-time autonomous learning in such data-rich environments is challenging, due to issues such as outliers, noisy sensory data, redundant and irrelevant dimensions, and the need for computational efficiency in real-time conditions. I introduce a set of automatic methods to address these challenges, using Bayesian inference—combined with variational approximations—in order to eliminate open parameters in a principled way. All these methods can be leveraged together to develop a Bayesian local kernel shaping for nonlinear regression. Bayesian local kernel shaping is computationally efficient, requires no sampling and automatically rejects outliers. It can be used for nonparametric regression with local polynomials (e.g., for real-time learning) or as a novel method to achieve nonstationary regression with Gaussian processes. The usefulness and improved performance of our algorithms are illustrated in various robotic applications such as parameter identification in robot dynamics, real-time outlier detection in tracking and learning a task-level control law.