# Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

arXiv: https://arxiv.org/abs/2309.13099 · HTML: https://mlsanity.com/paper/2309.13099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 74 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution](https://mlsanity.com/paper/1804.09081/) (2018)
- [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)
- [How morphological development can guide evolution](https://mlsanity.com/paper/1711.07387/) (2017)
- [Evolving embodied intelligence from materials to machines](https://mlsanity.com/paper/1901.05704/) (2019)
- [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)
- [Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment](https://mlsanity.com/paper/2104.03062/) (2021)

## Built on by (most cited first, 1 of 1)

- [An Empirical Study on the Computation Budget of Co-Optimization of Robot Design and Control in Simulation](https://mlsanity.com/paper/2409.08621/) (2024)

## Abstract (arXiv)

Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ``What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of `morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: `newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.