arXiv Machine Learning August 17, 2026 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 19

DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning

arXiv:2606. 19656v1 Announce Type: cross Abstract: A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience.

By Calvin Luo, Chen Sun, Shuran Song

agents diffusion reinforcement-learning robotics fine-tuning multimodal

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

arXiv AI

Jun 6

From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.

By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 21

Information-Based Exploration via Random Features for Reinforcement Learning

arXiv:2607. 17981v1 Announce Type: new Abstract: Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish.

By Waris Radji, Odalric-Ambrym Maillard

reinforcement-learning

arXiv Machine Learning

2d ago

Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration

arXiv:2604. 16509v2 Announce Type: replace-cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning.

By Adithya V. Sastry, Bibek Poudel, Weizi Li

llms agents reinforcement-learning robotics efficiency

arXiv Machine Learning

Jun 16

VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy

arXiv:2606. 14879v1 Announce Type: cross Abstract: Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks.

By Venkata Naren Devarakonda, Raktim Gautam Goswami, Prashanth Krishnamurthy, Farshad Khorrami

agents diffusion

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