Motivation and Objectives of GraphBot
Among the large family of machine learning techniques, the concept of probabilistic graphical models has gained significant interest in robotics, as it offers a good combination of graph-theoretic design and probabilistic reasoning together with a solid mathematical background. Due to their versatility, graphical models have become more and more attractive for robotics applications where probabilities are used to describe uncertainty about sensors and actions. In particular, graphical models such as Bayes nets, Markov random fields and factor graphs, have been proven to be very powerful tools in the area of robotic perception, scene analysis, and simultaneous localization and mapping (SLAM). The objective of this workshop is to collect, discuss, and analyze in detail recent approaches based on graphical models for typical robotics problems. Additionally, the workshop aims at bringing together researchers that are either interested or already working in this field, to talk about the successes, limitations, and open problems in the use of graphical models in robotics.
News and Announcements
Submission of extended abstracts:
Notification of acceptance:
Submission of full-length papers:
GraphBot 2010 is partly supported by the EC under contract number EUROPA-FP7-231888 .