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: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:2609.01351v1 Announce Type: cross
Abstract: Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimens...
By Jiho Lee, Nisar Ahmed, Kyle Hollins Wray, Zachary Sunberg
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
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: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
The paper presents an active inference route‑planning method for autonomous agents tasked with reconnaissance missions. It builds an evidence map that integrates both positive and negative sensor observations over time, using Dempster‑Shafer theory and a Gaussian sensor model to update a posterior probability distribution. By computing variational free energy across positions, the agent moves incrementally toward locations that minimize free energy, balancing exploration of large areas with exploitation of identified targets.
By Johan Schubert, Farzad Kamrani, Tove Gustavi
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
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
arXiv:2609.26021v1 Announce Type: new
Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...
By Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho
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
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