# Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites

arXiv: https://arxiv.org/abs/2504.06523 · HTML: https://mlsanity.com/paper/2504.06523/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning](https://mlsanity.com/paper/1511.06342/) (2015)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control](https://mlsanity.com/paper/2007.04976/) (2020)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [MetaMorph: Learning Universal Controllers with Transformers](https://mlsanity.com/paper/2203.11931/) (2022)
- [Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity](https://mlsanity.com/paper/1902.05546/) (2019)
- [My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control](https://mlsanity.com/paper/2010.01856/) (2020)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy](https://mlsanity.com/paper/2303.03832/) (2023)
- [MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics](https://mlsanity.com/paper/2012.04375/) (2020)
- [The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers](https://mlsanity.com/paper/2303.16207/) (2023)
- [Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots](https://mlsanity.com/paper/2402.09231/) (2024)
- [Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention](https://mlsanity.com/paper/2204.06481/) (2022)
- [Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots](https://mlsanity.com/paper/2306.09358/) (2023)
- [Co-evolving morphology and control of soft robots using a single genome](https://mlsanity.com/paper/2212.11517/) (2022)
- [Understanding fitness landscapes in morpho-evolution via local optima networks](https://mlsanity.com/paper/2402.07822/) (2024)

## Built on by (most cited first, 3 of 3)

- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Social Learning Strategies for Evolved Virtual Soft Robots](https://mlsanity.com/paper/2604.12482/) (2026)
- [Quality Diversity for Reliable Data Driven Time-Use Optimization](https://mlsanity.com/paper/2608.05230/) (2026)

## Abstract (arXiv)

Brain-body co-optimization suffers from fragile co-adaptation where brains become over-specialized for particular bodies, hindering their ability to transfer well to others. Evolutionary algorithms tend to discard such low-performing solutions, eliminating promising morphologies. Previous work considered applying MAP-Elites, where niche descriptors are based on morphological features, to promote better search over morphology space. In this work, we show that this approach still suffers from fragile co-adaptation: where a core mechanism of MAP-Elites, creating stepping stones through solutions that migrate from one niche to another, is disrupted. We suggest that this disruption occurs because the body mutations that move an offspring to a new morphological niche break the robots' fragile brain-body co-adaptation and thus significantly decrease the performance of those potential solutions -- reducing their likelihood of outcompeting an existing elite in that new niche. We utilize a technique, we call Pollination, that periodically replaces the controllers of certain solutions with a distilled controller with better generalization across morphologies to reduce fragile brain-body co-adaptation and thus promote MAP-Elites migrations. Pollination increases the success of body mutations and the number of migrations, resulting in better quality-diversity metrics. We believe we develop important insights that could apply to other domains where MAP-Elites is used.