arXiv Machine Learning By Ajith Anil Meera, Pablo Lanillos, Wouter Kouw

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

Read the original on arXiv Machine Learning →

arXiv:2608. 14466v1 Announce Type: cross Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Sep 10

Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation

The paper presents a deep active inference framework for real‑world robotic navigation that combines a diffusion policy with a multiple‑timescale recurrent state‑space model. The diffusion policy generates diverse candidate actions, while the state‑space model predicts long‑horizon outcomes, allowing the system to select actions that minimize expected free energy. Experiments show higher success rates and fewer collisions, especially in exploration‑heavy scenarios, demonstrating the effectiveness of this unified exploration and goal‑directed approach.

By Riko Yokozawa, Kentaro Fujii, Yuta Nomura, Shingo Murata
arXiv Statistics ML
3d ago

Learning to Plan from Random Exploration

arXiv:2609.38383v1 Announce Type: cross Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without po...

By Deqian Kong, Guangyan Sun, Sheng Cheng, Sirui Xie, Bo Pang, Jianwen Xie, Tony Geng, Caiwen Ding, Ying Nian Wu
arXiv Machine Learning
Sep 22

Computationally efficient safe exploration in reinforcement learning

The paper introduces “CoLSafe-MDP”, a reinforcement learning algorithm that ensures safe exploration in constrained Markov decision processes. It replaces computationally heavy Gaussian process methods with a Nadaraya-Watson estimator, achieving constant-time scaling for estimate bounds. The authors evaluate the algorithm on a grid-based environment and on observational Martian terrain data.

By Shreeram Murali, Shankar A. Deka, Dominik Baumann
arXiv AI
Jun 4

What Type of Inference is Active Inference?

arXiv:2606. 04935v1 Announce Type: new Abstract: Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior.

By Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar, Bert de Vries