The project is organised along 3 axes of research and development:

We will construct well-defined models of how probabilities may be represented and manipulated, and test predictions with psychophysical performance measures and studies of regional brain activation.
We expect to improve our understanding of neural mechanisms and derive new ideas for the implementation of probabilistic inference in engineering systems.

The main goal is to illustrate how probabilistic computation, and more specifically, Bayesian programming, may account for global behaviours of organisms in interaction with their environment.
We will focus on specific questions concerning multi-sensory perception and motion control.
We plan to develop new probabilistic models that explain the observed behaviours in human or animals and to implement them on autonomous artefacts.

We will use the Bayesian paradigm to develop an artefact that acquires repertoires of reactive probabilistic behaviours (synergies), builds combinations, hierarchies and temporal sequences of these behaviours (strategies) and can be trained for different tasks.
We shall imitate biology by reducing the amount of pre-specified knowledge of the environment built in by the designer, departing radically from classic robot design, and will explore the possibility for the artefact to "discover" part of its preliminary knowledge using evolutionary techniques.
We will evaluate the consequences of contraction theory. The result will be an artefact embodying efficient probabilistic algorithms for sensory interpretation and control, with more life-like behaviour and the means to test and develop further ideas.