Now showing items 11-20 of 59
Learning Discontinuities with Product-of-Sigmoids for Switching between Local Models
(ACM Press New York, 2005)
Sensorimotor data from many interesting physical interactions comprises discontinuities. While existing locally weighted learning approaches aim at learning smooth functions
Local Dimensionality Reduction for Non-Parametric Regression
Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many ...
A Novel Method for Learning Policies from Variable Constraint Data
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. Constraints are usually unobservable and frequently change between contexts. In this paper, we ...
Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
For stationary systems, efficient techniques for adaptive motor control exist which learn the system’s inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often ...
Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
We introduce a robust probabilistic approach to modeling shape contours based on a low- dimensional, nonlinear latent variable model. In contrast to existing techniques that use objective functions in data space without ...
Part-based Probabilistic Point Matching using Equivalence Constraints
Correspondence algorithms typically struggle with shapes that display part-based variation. We present a probabilistic approach that matches shapes using independent part transformations, where the parts themselves are ...
Learning Utility Surfaces for Movement Selection
Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the robot exceeding the dimensionality of the ...
Optimal Feedback Control for Anthropomorphic Manipulators
We study target reaching tasks of redundant anthropomorphic manipulators under the premise of minimal energy consumption and compliance during motion. We formulate this motor control problem in the framework of ...
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 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 ...