# Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration

arXiv: https://arxiv.org/abs/2608.14466 · HTML: https://mlsanity.com/paper/2608.14466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Reinforcement Learning through Active Inference](https://mlsanity.com/paper/2002.12636/) (2020)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2209.09185/) (2022)
- [Exploring and Learning Structure: Active Inference Approach in Navigational Agents](https://mlsanity.com/paper/2408.05982/) (2024)
- [Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes](https://mlsanity.com/paper/2306.01263/) (2023)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Navigation and Exploration with Active Inference: from Biology to Industry](https://mlsanity.com/paper/2508.07269/) (2025)
- [Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization](https://mlsanity.com/paper/2603.26339/) (2026)

## Abstract (arXiv)

An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected Free Energy (EFE), the principled action-selection objective from active inference, as a unifying criterion for budgeted robotic informative path planning. Maintaining a Gaussian-process belief over the information field, our agent plans continuous trajectories that minimize expected free energy under hard path-length constraints. The results from multiple realizations show that EFE-based planning yields accurate posterior maps and locates the highest-value regions simultaneously, outperforming information-theoretic baselines under the same settings. In robotic exploration, these unified, easy-to-tune principled information-gathering strategies facilitate autonomous deployment while enforcing efficiency and resource constraints.