# Comparing lifetime learning methods for morphologically evolving robots

arXiv: https://arxiv.org/abs/2203.03967 · HTML: https://mlsanity.com/paper/2203.03967/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2022.

## Built on by (most cited first, 6 of 6)

- [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)
- [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)

## Abstract (arXiv)

Evolving morphologies and controllers of robots simultaneously leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. We argue that this can be mitigated by having newborn robots perform a learning process that optimizes their inherited brain quickly after birth. We compare three different algorithms for doing this. To this end, we consider three algorithmic properties, efficiency, efficacy, and the sensitivity to differences in the morphologies of the robots that run the learning process.