# Active Inference in Robotics and Artificial Agents: Survey and Challenges

arXiv: https://arxiv.org/abs/2112.01871 · HTML: https://mlsanity.com/paper/2112.01871/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 122 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 30 of 48)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [The free energy principle for action and perception: A mathematical review](https://mlsanity.com/paper/1705.09156/) (2017)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Deep Active Inference as Variational Policy Gradients](https://mlsanity.com/paper/1907.03876/) (2019)
- [Reinforcement Learning through Active Inference](https://mlsanity.com/paper/2002.12636/) (2020)
- [Deep Active Inference](https://mlsanity.com/paper/1709.02341/) (2017)
- [On the Relationship Between Active Inference and Control as Inference](https://mlsanity.com/paper/2006.12964/) (2020)
- [Whence the Expected Free Energy?](https://mlsanity.com/paper/2004.08128/) (2020)
- [Variational Inference MPC for Bayesian Model-based Reinforcement Learning](https://mlsanity.com/paper/1907.04202/) (2019)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [End-to-End Pixel-Based Deep Active Inference for Body Perception and Action](https://mlsanity.com/paper/2001.05847/) (2019)
- [Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics](https://mlsanity.com/paper/2011.09756/) (2020)
- [Multimodal VAE Active Inference Controller](https://mlsanity.com/paper/2103.04412/) (2021)
- [Contrastive Active Inference](https://mlsanity.com/paper/2110.10083/) (2021)
- [Deep Active Inference for Partially Observable MDPs](https://mlsanity.com/paper/2009.03622/) (2020)
- [Stochastic Optimal Control as Approximate Input Inference](https://mlsanity.com/paper/1910.03003/) (2019)
- [Adaptive robot body learning and estimation through predictive coding](https://mlsanity.com/paper/1805.03104/) (2018)
- [Predictive Processing in Cognitive Robotics: a Review](https://mlsanity.com/paper/2101.06611/) (2021)
- [Bayesian policy selection using active inference](https://mlsanity.com/paper/1904.08149/) (2019)
- [A Novel Adaptive Controller for Robot Manipulators based on Active Inference](https://mlsanity.com/paper/1909.12768/) (2019)
- [Robot self/other distinction: active inference meets neural networks learning in a mirror](https://mlsanity.com/paper/2004.05473/) (2020)
- [Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control](https://mlsanity.com/paper/2103.06859/) (2021)
- [Active Inference for Integrated State-Estimation, Control, and Learning](https://mlsanity.com/paper/2005.05894/) (2020)
- [Active Inference or Control as Inference? A Unifying View](https://mlsanity.com/paper/2010.00262/) (2020)
- [Deep Active Inference for Autonomous Robot Navigation](https://mlsanity.com/paper/2003.03220/) (2020)
- [A deep active inference model of the rubber-hand illusion](https://mlsanity.com/paper/2008.07408/) (2020)

## Built on by (most cited first, 30 of 52)

- [Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?](https://mlsanity.com/paper/2202.09467/) (2022)
- [Neither hype nor gloom do DNNs justice](https://mlsanity.com/paper/2312.05355/) (2023)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [The Free Energy Principle for Perception and Action: A Deep Learning Perspective](https://mlsanity.com/paper/2207.06415/) (2022)
- [Active Inference as a Model of Agency](https://mlsanity.com/paper/2401.12917/) (2024)
- [Hierarchical generative modelling for autonomous robots](https://mlsanity.com/paper/2308.07775/) (2023)
- [Object-based active inference](https://mlsanity.com/paper/2209.01258/) (2022)
- [Interactive inference: a multi-agent model of cooperative joint actions](https://mlsanity.com/paper/2210.13113/) (2022)
- [Predictive Coding Networks and Inference Learning: Tutorial and Survey](https://mlsanity.com/paper/2407.04117/) (2024)
- [Masked Generative Priors Improve World Models Sequence Modelling Capabilities](https://mlsanity.com/paper/2410.07836/) (2024)
- [Disentangling Shape and Pose for Object-Centric Deep Active Inference Models](https://mlsanity.com/paper/2209.09097/) (2022)
- [Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2209.09185/) (2022)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents](https://mlsanity.com/paper/2203.10592/) (2022)
- [Value of Information and Reward Specification in Active Inference and POMDPs](https://mlsanity.com/paper/2408.06542/) (2024)
- [Deep Active Inference Agents for Delayed and Long-Horizon Environments](https://mlsanity.com/paper/2505.19867/) (2025)
- [Learning Policies for Continuous Control via Transition Models](https://mlsanity.com/paper/2209.08033/) (2022)
- [Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration](https://mlsanity.com/paper/2312.12583/) (2023)
- [Possible Principles for Aligned Structure Learning Agents](https://mlsanity.com/paper/2410.00258/) (2024)
- [Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation](https://mlsanity.com/paper/2211.10934/) (2022)
- [Object-Centric Scene Representations using Active Inference](https://mlsanity.com/paper/2302.03288/) (2023)
- [Adaptive Noise Covariance Estimation under Colored Noise using Dynamic Expectation Maximization](https://mlsanity.com/paper/2308.07797/) (2023)
- [Confidence-Aware Decision-Making and Control for Tool Selection](https://mlsanity.com/paper/2403.03808/) (2024)
- [Free Energy Projective Simulation (FEPS): Active inference with interpretability](https://mlsanity.com/paper/2411.14991/) (2024)
- [Object-centric proto-symbolic behavioural reasoning from pixels](https://mlsanity.com/paper/2411.17438/) (2024)
- [Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Study](https://mlsanity.com/paper/2502.04249/) (2025)
- [Active Inference for Energy Control and Planning in Smart Buildings and Communities](https://mlsanity.com/paper/2503.18161/) (2025)
- [EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization](https://mlsanity.com/paper/2506.17516/) (2025)
- [Deriving time-averaged active inference from control principles](https://mlsanity.com/paper/2208.10601/) (2022)

## Abstract (arXiv)

Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it has been shown to be a promising approach to the problems of state-estimation and control under uncertainty, as well as a foundation for the construction of goal-driven behaviours in robotics and artificial agents in general. Here, we review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning; describing current achievements with a particular focus on robotics. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness. Furthermore, we connect this approach with other frameworks and discuss its expected benefits and challenges: a unified framework with functional biological plausibility using variational Bayesian inference.