Cel
Despite very extensive research efforts contemporary robots and other cognitive artifacts are not yet ready to autonomously operate in complex real world environments. One of the major reasons for this failure in creating cognitive situated systems is the difficulty in the handling of incomplete knowledge and uncertainty.
In this project we will investigate and apply Bayesian models and approaches in order to develop artificial cognitive systems that can carry out complex tasks in real world environments. We will take inspiration from the brains of mammals including humans and apply our findings to the developments of cognitive systems.
The Bayesian approach will be used to model different levels of brain function, from neural functions up to complex behaviours. This will enable us to show that neural functions and higher-level cognitive activities can coherently be modelled within the Bayesian framework. The Bayesian models will be validated and adapted as necessary according to neuro-physiological data from rats and humans and through psychophysical experiments on human.
The Bayesian approach will also be used to develop four artificial cognitive systems concerned with
- autonomous navigation,
- multi-modal perception and reconstruction of the environment,
- Semantic facial motion tracking, and
- human body motion recognition and behaviour analysis.
The conducted research shall result in a consistent Bayesian framework offering enhanced tools for probabilistic reasoning in complex real world situations. The performance will be demonstrated through its applications to drive assistant systems and 3D mapping, both very complex real world tasks.