# Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential

arXiv: https://arxiv.org/abs/2508.17464 · HTML: https://mlsanity.com/paper/2508.17464/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2025.

## Built on by (most cited first, 4 of 4)

- [Social Learning Strategies for Evolved Virtual Soft Robots](https://mlsanity.com/paper/2604.12482/) (2026)
- [Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics](https://mlsanity.com/paper/2605.15769/) (2026)
- [Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning](https://mlsanity.com/paper/2608.23100/) (2026)
- [RoboLDA: A Probabilistic Generative Model for Uncovering Embodied Hierarchical Structures in Voxel-based Soft Robots](https://mlsanity.com/paper/2609.29490/) (2026)

## Abstract (arXiv)

Brain-body co-optimization remains a challenging problem. To understand and overcome its challenges, we exhaustively map a morphology-fitness landscape: we train controllers for each morphology in a design space of 1,305,840 voxel-based soft robots. We show that this design space constitutes a good model for studying brain-body co-optimization and that our mapping roughly captures its landscape. Complete knowledge of the landscape lets us analyze how evolutionary co-optimization algorithms unfold. We find that the tested algorithms cannot consistently find near-optimal solutions: the search, at times, gets stuck on morphologies one mutation away from better ones, because it regularly undervalues individuals with newly mutated bodies and eliminates promising morphologies. On the other hand, co-optimizing morphology and control creates useful goal-switching, yielding morphology-controller pairs whose performance cannot be reached by optimizing the controller alone for a fixed morphology. Together, these results ground trends in the literature and offer insights for future work.