SOLAR-GP: Sparse online locally adaptive regression using Gaussian processes for Bayesian robot model learning and control

IEEE Robotics and Automation Letters, 2020

Brian Wilcox, Michael C Yip

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9000569/

Abstract: Machine learning methods have been widely used in robot control to learn inverse mappings. These methods are used to capture the entire non-linearities and non-idealities of a system that make geometric or phenomenological modeling difficult. Most methods employ some form of off-line or batch learning where training may be performed prior to a task, or in an intermittent manner, respectively. These strategies are generally unsuitable for teleoperation, where commands and sensor data are received in sequential streams and models must be learned on-the-fly. We combine sparse, local, and streaming methods to form Sparse Online Locally Adaptive Regression using Gaussian Processes (SOLAR-GP), which trains streaming data on localized sparse Gaussian Process models and infers a weighted local function mapping of the robot sensor states to joint states. The resultant prediction of the teleoperation …

Wilcox et al. (2020) SOLAR-GP: Sparse online locally adaptive regression using Gaussian processes for Bayesian robot model learning and control, IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2832-2839.