# Deep Active Inference for Autonomous Robot Navigation

arXiv: https://arxiv.org/abs/2003.03220 · HTML: https://mlsanity.com/paper/2003.03220/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Active inference, Bayesian optimal design, and expected utility](https://mlsanity.com/paper/2110.04074/) (2021)
- [Sophisticated Learning: A novel algorithm for active learning during model-based planning](https://mlsanity.com/paper/2308.08029/) (2023)
- [Robot Localization and Navigation through Predictive Processing using LiDAR](https://mlsanity.com/paper/2109.04139/) (2021)
- [Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control](https://mlsanity.com/paper/2503.01676/) (2025)
- [Deep Active Inference with Diffusion Policy and Multiple Timescale World Model for Real-World Exploration and Navigation](https://mlsanity.com/paper/2510.23258/) (2025)
- [Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control](https://mlsanity.com/paper/2407.07684/) (2024)
- [Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds](https://mlsanity.com/paper/2605.16030/) (2026)

## Abstract (arXiv)

Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal generative model to drive agents towards minimal surprise. Although this theory has shown interesting results with grounding in cognitive neuroscience, its application remains limited to simulations with small, predefined sensor and state spaces. In this paper, we leverage recent advances in deep learning to build more complex generative models that can work without a predefined states space. State representations are learned end-to-end from real-world, high-dimensional sensory data such as camera frames. We also show that these generative models can be used to engage in active inference. To the best of our knowledge this is the first application of deep active inference for a real-world robot navigation task.