# Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments

arXiv: https://arxiv.org/abs/2403.19545 · HTML: https://mlsanity.com/paper/2403.19545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [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)
- [Comparing lifetime learning methods for morphologically evolving robots](https://mlsanity.com/paper/2203.03967/) (2022)
- [Towards the Targeted Environment-Specific Evolution of Robot Components](https://mlsanity.com/paper/1810.04735/) (2018)

## Built on by (most cited first, 2 of 2)

- [Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics](https://mlsanity.com/paper/2605.15769/) (2026)
- [A Fresh Look at Lamarckian Evolution and the Baldwin Effect](https://mlsanity.com/paper/2605.28703/) (2026)

## Abstract (arXiv)

This study explores the integration of Lamarckian system into evolutionary robotics (ER), comparing it with the traditional Darwinian model across various environments. By adopting Lamarckian principles, where robots inherit learned traits, alongside Darwinian learning without inheritance, we investigate adaptation in dynamic settings. Our research, conducted in six distinct environmental setups, demonstrates that Lamarckian systems outperform Darwinian ones in adaptability and efficiency, particularly in challenging conditions. Our analysis highlights the critical role of the interplay between controller & morphological evolution and environment adaptation, with parent-offspring similarities and newborn &survivors before and after learning providing insights into the effectiveness of trait inheritance. Our findings suggest Lamarckian principles could significantly advance autonomous system design, highlighting the potential for more adaptable and robust robotic solutions in complex, real-world applications. These theoretical insights were validated using real physical robots, bridging the gap between simulation and practical application.