# The Effects of Learning in Morphologically Evolving Robot Systems

arXiv: https://arxiv.org/abs/2111.09851 · HTML: https://mlsanity.com/paper/2111.09851/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.01432/) (2018)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning](https://mlsanity.com/paper/1911.06832/) (2019)
- [Neural Graph Evolution: Towards Efficient Automatic Robot Design](https://mlsanity.com/paper/1906.05370/) (2019)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Data-efficient Learning of Morphology and Controller for a Microrobot](https://mlsanity.com/paper/1905.01334/) (2019)
- [MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics](https://mlsanity.com/paper/2012.04375/) (2020)
- [Morpho-evolution with learning using a controller archive as an inheritance mechanism](https://mlsanity.com/paper/2104.04269/) (2021)
- [Differential Evolution with Reversible Linear Transformations](https://mlsanity.com/paper/2002.02869/) (2020)
- [Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations](https://mlsanity.com/paper/1805.03388/) (2018)
- [The Dynamic of Body and Brain Co-Evolution](https://mlsanity.com/paper/2011.11440/) (2020)
- [Gait-learning with morphologically evolving robots generated by L-system](https://mlsanity.com/paper/2107.08249/) (2021)

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

- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies](https://mlsanity.com/paper/2309.13908/) (2023)
- [Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments](https://mlsanity.com/paper/2403.19545/) (2024)
- [Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better](https://mlsanity.com/paper/2309.13099/) (2023)
- [Efficient and Diverse Generative Robot Designs using Evolution and Intrinsic Motivation](https://mlsanity.com/paper/2411.18423/) (2024)
- [Social Learning Strategies for Evolved Virtual Soft Robots](https://mlsanity.com/paper/2604.12482/) (2026)
- [Exploring Robot Morphology Spaces through Breadth-First Search and Random Query](https://mlsanity.com/paper/2309.14387/) (2023)
- [An Empirical Study on the Computation Budget of Co-Optimization of Robot Design and Control in Simulation](https://mlsanity.com/paper/2409.08621/) (2024)
- [More complex environments may be required to discover benefits of lifetime learning in evolving robots](https://mlsanity.com/paper/2412.16184/) (2024)
- [Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization](https://mlsanity.com/paper/2604.20365/) (2026)
- [Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics](https://mlsanity.com/paper/2605.15769/) (2026)

## Abstract (arXiv)

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period by the so-called Triangle of Life framework has been proposed relatively long ago. However, an empirical assessment is still lacking to-date. In this paper we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, although learning only directly affects the controllers, we demonstrate that the evolved morphologies will be also different. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the concept of morphological intelligence quantified by the ability of a given body to learn. We observe that the learning delta, the performance difference between the inherited and the learned brain, is growing throughout the evolutionary process. This shows that evolution is producing robots with an increasing plasticity, that is, consecutive generations are becoming better and better learners which in turn makes them better and better at the given task. All in all, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system architecture with practical benefits.