Now showing items 21-30 of 59
Incremental Online Learning in High Dimensions
(MIT Press, 2005-12)
Locally weighted projection regression (LWPR) is a new algorithm for incremental non-linear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control
The least squares approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular and useful choice as a basis function. However, ...
Reinforcement Learning for Humanoid Robots - Policy Gradients and Beyond
Reinforcement learning offers one of the most general frameworks to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like ...
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 ...
Learning Nullspace Policies
Many everyday tasks performed by people, such as reaching, pointing or drawing, resolve redundant degrees of freedom in the arm in a similar way. In this paper we present a novel method for learning the strategy used ...
Scaling Reinforcement Learning Paradigms for Motor Control
Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems ...
Robustness of VOR and OKR adaptation under kinematics and dynamics transformations
Many computational models of vestibulo-ocular reflex (VOR) adaptation have been proposed, however none of these models have explicitly highlighted the distinction between adaptation to dynamics transformations, in which ...
Kernel Carpentry for Online Regression using Randomly Varying Coefficient Model
We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this