An information theoretic approach to autonomous learning of embodied agents

• Applicant: Professor Dr. Nihat Ay
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Human Cognitive and Systems Neuroscience ; Mathematics ; Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: from 2011 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 200306544

Ideally, autonomously learning systems should be able to gather enough information about themselves and their environment to solve a given task. This explicitly means that they should not rely on a human supervisor. The main question therefore is: How can a system that ist not equipped with a priori knowledge about itself and the environment identify its task and learn to optimally solve it, if it only occasionally receives a yes-no-feedback. This type of system is desired for operations in unknown and dynamic environments. To be able to function in such environments, the system must also detect disturbances, like a blockage of one wheel, and find a way to overcome such an impairment. The ability of autonomous learning will become more and more essential as the complexity of todays robotic systems already challenge the classical programming approach. In this project, we will derive the mathematical methods which will help to realise the mentioned capacities in artificial systems. Our main concerns are theory construction and basic research. We believe that there are three main aspects that we have to consider in the context of autonomous learning. First, the learning system must have a form of internal motivation to explore its body and environment; exploration is a central element of this project. Second, the exploration must be guided by the physical constraints of the body and environment. Not every action is possible at every point in time. To autonomously recognise which actions are effective and when will improve the learning as it reduces the search space significantly. Third, the internal motivation must be combined effectively with an external feedback signal. In this context, we believe that the field of embodied autonomous learning requires new methods from information theory and information geometry to make the next big step towards autonomy. In this project, we will investigate the theoretical foundations of autonomous learning and validate them in virtual robotic systems.

• DFG Programme: Priority Programmes
• Subproject of: SPP 1527: Autonomous Learning