Vieri Giuliano Santucci


Vieri Giuliano Santucci is a researcher at the Institute of Cognitive Sciences and Technologies (CNR, Rome). He holds a Ph.D. in computer science at the University of Plymouth (UK), an M.S. degree in theories and techniques of knowledge, faculty of Philosophy (University of Rome “La Sapienza”) and a B.Sc. degree in Philosophy (University of Pisa). His interests range from autonomous open-ended learning processes to motivations in both biological and artificial agents, as well as to the impact of new technologies on society and cognition. His current work focuses on the development of robotic architectures allowing artificial agents to autonomously improve their competences on the basis of the biologically-inspired construct of intrinsic motivations. He published in peer-reviewed journals and attended many international conferences, and actively contributed to the European Projects ‘IM-CLeVeR – Intrinsically-Motivated Cumulative-Learning Versatile Robots’ and ‘GOAL – Robots’, within which he began to develop the GRAIL architecture. 


Hierarchical robotic architectures for the autonomous learning of multiple (interrelated) tasks

Autonomously learning multiple tasks in potentially unknown and unstructured environments is a paramount challenge for the development of versatile and adaptive artificial agents that have to be employed in real world scenarios. Even more interesting are the scenarios in which interdependencies exist between the different goals, forcing the robot not only to acquire the low-level skills necessary to reach the different desired states, but also the sequence of tasks that determine the preconditions necessary to obtain the goals themselves. In this presentation we will face these issues from an architectural perspective, presenting the last developments of GRAIL architecture, and showing how dividing the different learning processes into a hierarchy of mechanisms helps the robot in autonomously learning different interdependent tasks, even in scenarios where interdependencies between goals might change over time.

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