mlsanityData to 14 Oct 2026

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

arXiv 2504.06523 ↗First posted 9 Apr 2025cs.RO cs.LG cs.NEcited by 3

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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.

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

19 of 34 references.

References · 19

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2015Policy Distillationcited by 440
  3. 2015Actor-Mimic: Deep Multitask and Transfer Reinforcement Learningcited by 178
  4. 2021Embodied Intelligence via Learning and Evolutioncited by 163
  5. 2020One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Controlcited by 121
  6. 2017Quality and Diversity Optimization: A Unifying Modular Frameworkcited by 113
  7. 2022MetaMorph: Learning Universal Controllers with Transformerscited by 89
  8. 2019Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularitycited by 70
  9. 2020My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Controlcited by 70
  10. 2022Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robotscited by 47
  11. 2017Scalable Co-Optimization of Morphology and Control in Embodied Machinescited by 40
  12. 2023MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policycited by 20
7 more
  1. 2020MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Roboticscited by 11
  2. 2023The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformerscited by 10
  3. 2024Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robotscited by 9
  4. 2022Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attentioncited by 8
  5. 2023Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robotscited by 7
  6. 2022Co-evolving morphology and control of soft robots using a single genomecited by 5
  7. 2024Understanding fitness landscapes in morpho-evolution via local optima networkscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.