# Evolving embodied intelligence from materials to machines

arXiv: https://arxiv.org/abs/1901.05704 · HTML: https://mlsanity.com/paper/1901.05704/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [From Efficient Multimodal Models to World Models: A Survey](https://mlsanity.com/paper/2407.00118/) (2024)
- [Vision-Language Navigation with Embodied Intelligence: A Survey](https://mlsanity.com/paper/2402.14304/) (2024)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [Teaching Robots to Build Simulations of Themselves](https://mlsanity.com/paper/2311.12151/) (2023)
- [Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design](https://mlsanity.com/paper/2311.00462/) (2023)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better](https://mlsanity.com/paper/2309.13099/) (2023)
- [Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM](https://mlsanity.com/paper/2311.12477/) (2023)
- [RAPID: Reconfigurable, Adaptive Platform for Iterative Design](https://mlsanity.com/paper/2602.06653/) (2026)

## Abstract (arXiv)

Natural lifeforms specialise to their environmental niches across many levels; from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs, and overarching body plans. We propose Multi-Level Evolution (MLE), a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions. MLE concurrently explores constituent molecular and material 'building blocks', as well as their possible assemblies into specialised morphological and sensorimotor configurations. MLE provides a route to fully harness a recent explosion in available candidate materials and ongoing advances in rapid manufacturing processes. We outline a feasible MLE architecture that realises this vision, highlight the main roadblocks and how they may be overcome, and show robotic applications to which MLE is particularly suited. By forming a research agenda to stimulate discussion between researchers in related fields, we hope to inspire the pursuit of multi-level robotic design all the way from material to machine.