Expected Free Energy provides a single, tunable objective that lets robots efficiently explore unknown environments by balancing information gathering with resource constraints—useful for real-world autonomous missions.
This paper applies Expected Free Energy from active inference to robotic path planning for Mars exploration. The robot must balance two goals: building accurate maps and finding high-value regions (like water sources) while staying within travel and measurement budgets. The approach outperforms traditional information-seeking methods by unifying both objectives under one principled framework.