A Motivation-Driven Incremental Learning Framework for Robotics
Letícia Mara Berto, Ricardo Gudwin, Esther Luna Colombini · 2025
This thesis presents a computational framework for intrinsically motivated autonomous agents, formalizing intrinsic motivation (IM) as a multi-objective reinforcement learning problem integrating drive regulation, hedonic valuation, hierarchical need prioritization, and Theory of Mind, enabling adaptive, long-term decision-making and socially aware interaction. Validated on simulated and physical robotic platforms, the framework demonstrates improved behavioral stability, policy reshaping via hedonic modulation, and enhanced cooperation among agents with compatible motivational profiles and one altruistic agent. This work extends beyond robotics, offering a computational model of IM that bridges cognitive science and autonomous decision-making.