Integrating Open-Ended Learning in the Sense-Plan-Act Robot Control Paradigm*

Abstract

This paper presents the achievements obtained from a study performed within the IMPACT (Intrinsically Motivated Planning Architecture for Curiosity-driven roboTs) Project funded by the European Space Agency (ESA).The main contribution of the work is the realization of an innovative robotic architecture in which the well-known three-layered architectural paradigm (decisional, executive, and functional) for controlling robotic systems is enhanced with autonomous learning capabilities.The architecture is the outcome of the application of an interdisciplinary approach integrating Artificial Intelligence (AI), Autonomous Robotics, and Machine Learning (ML) techniques.In particular, state-of-the-art AI planning systems and algorithms were integrated with Reinforcement Learning (RL) algorithms guided by intrinsic motivations (curiosity, exploration, novelty, and surprise).The aim of this integration was to: (i) develop a software system that allows a robotic platform to autonomously represent in symbolic form the skills autonomously learned through intrinsic motivations; (ii) show that the symbolic representation can be profitably used for automated planning purposes, thus improving the robot's exploration and knowledge acquisition capabilities.The proposed solution is validated in a test scenario inspired by a typical space exploration mission involving a rover.