We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a...
Gabriele Peters, Claus-Peter Alberts, Markus Bries...
This paper addresses the measurement of motion expressiveness in wheeled mobile robots. A neural network based supervised learning strategy is proposed as a method to fuse informat...
Abstract. A biologically inspired computational model of rodent representation–based (locale) navigation is presented. The model combines visual input in the form of realistic tw...
Denis Sheynikhovich, Ricardo Chavarriaga, Thomas S...
Time varying environments or model selection problems lead to crucial dilemmas in identification and control science. In this paper, we propose a modular prediction scheme consisti...
Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn m...