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

• URL: http://arxiv.org/abs/2510.23258v1
• Date: Mon, 27 Oct 2025 12:21:33 GMT
• Title: Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation
• Authors: Riko Yokozawa, Kentaro Fujii, Yuta Nomura, Shingo Murata,
• Abstract summary: AIF based on the free-energy principle provides a unified framework for exploration and goal-directed navigation.<n>We show how AIF based on EFE minimization can unify exploration and goal-directed navigation in real-world robotic settings.
• Score: 0.5933113619360969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed navigation in order to reach specified targets. Active inference (AIF) based on the free-energy principle provides a unified framework for these behaviors by minimizing the expected free energy (EFE), thereby combining epistemic and extrinsic values. To realize this practically, we propose a deep AIF framework that integrates a diffusion policy as the policy model and a multiple timescale recurrent state-space model (MTRSSM) as the world model. The diffusion policy generates diverse candidate actions while the MTRSSM predicts their long-horizon consequences through latent imagination, enabling action selection that minimizes EFE. Real-world navigation experiments demonstrated that our framework achieved higher success rates and fewer collisions compared with the baselines, particularly in exploration-demanding scenarios. These results highlight how AIF based on EFE minimization can unify exploration and goal-directed navigation in real-world robotic settings.

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