We propose a generative statistical approach to human motion modeling and tracking that utilizes probabilistic latent semantic (PLSA) models to describe the mapping of image featu...
— An important problem in robotics is planning and selecting actions for goal-directed behavior in noisy uncertain environments. The problem is typically addressed within the fra...
We present a mixture model based approach for learning individualized behavior models for the Web users. We investigate the use of maximum entropy and Markov mixture models for ge...
In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo ...
Ioannis M. Rekleitis, Gregory Dudek, Evangelos E. ...
This paper describes the foundations and algorithms of a new probabilistic roadmap (PRM) planner that is: (1) singlequery –i.e., it does not pre-compute a roadmap, but uses the ...