Motivational System for the Discovery of Missions in Autonomous Robots

Abstract

[Abstract] This work introduces a motivational system for autonomous robots that allows the generation and learning of missions (and their associated drives) aligned with human purposes. The system is integrated into the e-MDB cognitive architecture, employing Large Language Models (LLMs) to interpret natural language instructions. The system employs three distinct LLM instances, each specialized for a specific function: semantic alignment, mission generation, and modeling of motivational drives. Experiments conducted within the Gazebo simulation environment demonstrated consistent alignment between robot behavior and human purposes, as well as high reliability in mission and drive validity.