# Discovering Sensorimotor Agency in Cellular Automata using Diversity Search

arXiv: https://arxiv.org/abs/2402.10236 · HTML: https://mlsanity.com/paper/2402.10236/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 75 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions](https://mlsanity.com/paper/1901.01753/) (2019)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Automatic Curriculum Learning For Deep RL: A Short Survey](https://mlsanity.com/paper/2003.04664/) (2020)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning](https://mlsanity.com/paper/1708.02190/) (2017)
- [CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning](https://mlsanity.com/paper/1810.06284/) (2018)
- [Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration](https://mlsanity.com/paper/2002.09253/) (2020)
- [Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots](https://mlsanity.com/paper/1301.4862/) (2013)
- [FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1804.11147/) (2018)
- [Cellular automata as convolutional neural networks](https://mlsanity.com/paper/1809.02942/) (2018)
- [Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and minds](https://mlsanity.com/paper/2201.10346/) (2021)
- [Lenia and Expanded Universe](https://mlsanity.com/paper/2005.03742/) (2020)
- [Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent](https://mlsanity.com/paper/2106.15240/) (2021)
- [Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems](https://mlsanity.com/paper/1908.06663/) (2019)
- [Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems](https://mlsanity.com/paper/2007.01195/) (2020)
- [Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization](https://mlsanity.com/paper/2212.07906/) (2022)
- [Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata](https://mlsanity.com/paper/2304.05639/) (2023)
- [Interpreting systems as solving POMDPs: a step towards a formal understanding of agency](https://mlsanity.com/paper/2209.01619/) (2022)
- [Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems](https://mlsanity.com/paper/1605.05676/) (2016)

## Built on by (most cited first, 9 of 9)

- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)
- [A Path to Universal Neural Cellular Automata](https://mlsanity.com/paper/2505.13058/) (2025)
- [Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics](https://mlsanity.com/paper/2505.15998/) (2025)
- [Flow-Lenia: Emergent evolutionary dynamics in mass conservative continuous cellular automata](https://mlsanity.com/paper/2506.08569/) (2025)
- [Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata](https://mlsanity.com/paper/2509.03863/) (2025)
- [Emergence of Implicit World Models from Mortal Agents](https://mlsanity.com/paper/2411.12304/) (2024)
- [Complex System Exploration with Interactive Human Guidance](https://mlsanity.com/paper/2510.00794/) (2025)
- [Balancing Centralized Learning and Distributed Self-Organization: A Hybrid Model for Embodied Morphogenesis](https://mlsanity.com/paper/2511.10101/) (2025)
- [The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning](https://mlsanity.com/paper/2608.26116/) (2026)

## Abstract (arXiv)

The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open-question has been whether it it is possible to find environment rules that self-organize robust "individuals" from an initial state with no prior existence of things like "bodies", "brain", "perception" or "action". In this paper, we leverage recent advances in machine learning, combining algorithms for diversity search, curriculum learning and gradient descent, to automate the search of such "individuals", i.e. localized structures that move around with the ability to react in a coherent manner to external obstacles and maintain their integrity, hence primitive forms of sensorimotor agency. We show that this approach enables to find systematically environmental conditions in CA leading to self-organization of such basic forms of agency. Through multiple experiments, we show that the discovered agents have surprisingly robust capabilities to move, maintain their body integrity and navigate among various obstacles. They also show strong generalization abilities, with robustness to changes of scale, random updates or perturbations from the environment not seen during training. We discuss how this approach opens new perspectives in AI and synthetic bioengineering.