Now showing items 1-6 of 6
Bayesian Kernel Shaping for Learning Control
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of output noise varies spatially. Previous ...
Active Estimation of Object Dynamics Parameters with Tactile Sensors
The estimation of parameters that affect the dynamics of objects—such as viscosity or internal degrees of freedom—is an important step in autonomous and dexterous robotic manipulation of objects. However, accurate and ...
Efficient Learning and Feature Selection in High Dimensional Regression
(MIT Press, 2010)
We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a ...
Active sequential learning with tactile feedback
We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and high- dimensional, collecting enough representative data ...
A Bayesian Approach to Empirical Local Linearization For Robotics
Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics systems where the dynamics and kinematics are ...