H-GRAIL: A Robotic Motivational Architecture to Tackle Open-Ended Learning Challenges

This article addresses the challenge of developing artificial agents capable of autonomously discovering interesting environmental states, setting them as goals, and learning the necessary skills and curricula to achieve these goals - an essential requirement for deploying robotic systems in real-world scenarios. In such environments, robots must adapt to unforeseen situations, learn new skills, and manage unexpected changes autonomously, which is central to open-ended learning (OEL). We present hierarchical goal-discovery robotic architecture for intrinsically-motivated learning (H-GRAIL) an architecture designed to foster autonomous OEL in robotic agents. The novelty of H-GRAIL compared to existing approaches, which often address isolated challenges in OEL, is that it integrates multiple mechanisms that enable robots to autonomously discover new goals, acquire skills, and manage learning processes in dynamic, nonstationary environments. We present tests that demonstrate the advantages of this approach in enabling robots to achieve different goals in nonstationary environments and simultaneously address many of the challenges inherent to OEL.