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

arXiv:2510.23258 · cs.RO, cs.AI, cs.LG · Submitted 2025-10-27 · Read on arXiv

cs.RO, cs.AI, cs.LG

Submitted: 2025-10-27

Updated: 2026-09-07

Comments: Preprint version

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 42× in this paper · explained in 7 episodes
• Active Inference — 21× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Unified Framework — 3× in this paper · explained in 5 episodes
• Active Exploration — 2× in this paper · explained in 2 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Latent Dynamics — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Computational Biology — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Information Gain — 1× in this paper · explained in 2 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• ViNT: A Foundation Model for Visual Navigation
• DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation
• DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
• NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
• Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control
• DayDreamer: World Models for Physical Robot Learning
• Learning Latent Dynamics for Planning from Pixels
• Gradient-based Planning with World Models
• Sparse Imagination for Efficient Visual World Model Planning
• X-MOBILITY: End-To-End Generalizable Navigation via World Modeling
• Learning World Models for Unconstrained Goal Navigation
• Navigation World Models
• WMNav: Integrating Vision-Language Models into World Models for Object Goal Navigation
• Tracking and Planning with Spatial World Models
• GAIA-1: A Generative World Model for Autonomous Driving
• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
• Deep Active Inference for Autonomous Robot Navigation
• Bio-Inspired Topological Autonomous Navigation with Active Inference in Robotics
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Denoising Diffusion Implicit Models

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