Evolving embodied intelligence from materials to machines
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 2; 2025: 2; 2026: 1.
11 papers.
- 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
- 2024From Efficient Multimodal Models to World Models: A Surveycited by 13
- 2024Vision-Language Navigation with Embodied Intelligence: A Surveycited by 9
- 2022A Rubric for Human-like Agents and NeuroAIcited by 8
- 2023Teaching Robots to Build Simulations of Themselvescited by 6
- 2023Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Designcited by 4
- 2025Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challengescited by 4
- 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutionscited by 2
- 2023Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Bettercited by 1
- 2023Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEMcited by 0
- 2026RAPID: Reconfigurable, Adaptive Platform for Iterative Designcited by 0
- 2026RAPID: Reconfigurable, Adaptive Platform for Iterative Design6 Feb 2026
- 2025Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges4 Dec 2025
- 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions21 Apr 2025
- 2024From Efficient Multimodal Models to World Models: A Survey27 Jun 2024
- 2024Vision-Language Navigation with Embodied Intelligence: A Survey22 Feb 2024
- 2023Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM21 Nov 2023
- 2023Teaching Robots to Build Simulations of Themselves20 Nov 2023
- 2023Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design1 Nov 2023
- 2023Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better22 Sep 2023
- 2022A Rubric for Human-like Agents and NeuroAI8 Dec 2022
- 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence28 Oct 2021