# Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents

arXiv: https://arxiv.org/abs/2309.05999 · HTML: https://mlsanity.com/paper/2309.05999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 187 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [Large-Scale Study of Curiosity-Driven Learning](https://mlsanity.com/paper/1808.04355/) (2018)
- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
- [Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey](https://mlsanity.com/paper/2012.09830/) (2020)
- [Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations](https://mlsanity.com/paper/1308.3513/) (2013)
- [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)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [On the Relationship Between Active Inference and Control as Inference](https://mlsanity.com/paper/2006.12964/) (2020)
- [Context-Aware Safe Reinforcement Learning for Non-Stationary Environments](https://mlsanity.com/paper/2101.00531/) (2021)
- [The free energy principle made simpler but not too simple](https://mlsanity.com/paper/2201.06387/) (2022)
- [Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration](https://mlsanity.com/paper/1803.00781/) (2018)
- [Emotion in Reinforcement Learning Agents and Robots: A Survey](https://mlsanity.com/paper/1705.05172/) (2017)
- [Path integrals, particular kinds, and strange things](https://mlsanity.com/paper/2210.12761/) (2022)
- [Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours](https://mlsanity.com/paper/1812.09113/) (2018)
- [Testing Stationarity and Change Point Detection in Reinforcement Learning](https://mlsanity.com/paper/2203.01707/) (2022)
- [Context Meta-Reinforcement Learning via Neuromodulation](https://mlsanity.com/paper/2111.00134/) (2021)
- [Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks](https://mlsanity.com/paper/1705.07241/) (2017)
- [Active Inference or Control as Inference? A Unifying View](https://mlsanity.com/paper/2010.00262/) (2020)
- [Self-Organization and Artificial Life: A Review](https://mlsanity.com/paper/1804.01144/) (2018)
- [On Reward Function for Survival](https://mlsanity.com/paper/1606.05767/) (2016)
- [Neural dynamics of emotion and cognition: from trajectories to underlying neural geometry](https://mlsanity.com/paper/1902.00337/) (2019)
- [Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents](https://mlsanity.com/paper/2109.06580/) (2021)

## Built on by (most cited first, 1 of 1)

- [CARD: Towards Conditional Design of Multi-agent Topological Structures](https://mlsanity.com/paper/2603.01089/) (2026)

## Abstract (arXiv)

Building autonomous -- i.e., choosing goals based on one's needs -- and adaptive -- i.e., surviving in ever-changing environments -- agents has been a holy grail of artificial intelligence (AI). A living organism is a prime example of such an agent, offering important lessons about adaptive autonomy. Here, we focus on interoception, a process of monitoring one's internal environment to keep it within certain bounds, which underwrites the survival of an organism. To develop AI with interoception, we need to factorize the state variables representing internal environments from external environments and adopt life-inspired mathematical properties of internal environment states. This paper offers a new perspective on how interoception can help build autonomous and adaptive agents by integrating the legacy of cybernetics with recent advances in theories of life, reinforcement learning, and neuroscience.