A hierarchical robot control architecture that supports learning of skills at different levels through “curriculum learning” and an interesting approach to mix behaviours

Suro, F., Ferber, J., Stratulat, T. et al., A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents, . Auton Robot 45, 245–264 (2021) DOI: 10.1007/s10514-020-09960-7.

One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.

Comments are closed.

Post Navigation