# Scalable Co-Optimization of Morphology and Control in Embodied Machines

arXiv: https://arxiv.org/abs/1706.06133 · HTML: https://mlsanity.com/paper/1706.06133/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 30 of 40)

- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [MetaMorph: Learning Universal Controllers with Transformers](https://mlsanity.com/paper/2203.11931/) (2022)
- [Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design](https://mlsanity.com/paper/2110.03659/) (2021)
- [Task-Agnostic Morphology Evolution](https://mlsanity.com/paper/2102.13100/) (2021)
- [Learning Modular Robot Control Policies](https://mlsanity.com/paper/2105.10049/) (2021)
- [Symmetry-Aware Robot Design with Structured Subgroups](https://mlsanity.com/paper/2306.00036/) (2023)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [Morpho-evolution with learning using a controller archive as an inheritance mechanism](https://mlsanity.com/paper/2104.04269/) (2021)
- [MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy](https://mlsanity.com/paper/2309.17227/) (2023)
- [Reinforcement learning for freeform robot design](https://mlsanity.com/paper/2310.05670/) (2023)
- [Environment Optimization for Multi-Agent Navigation](https://mlsanity.com/paper/2209.11279/) (2022)
- [Constrained Environment Optimization for Prioritized Multi-Agent Navigation](https://mlsanity.com/paper/2305.11260/) (2023)
- [Accelerated co-design of robots through morphological pretraining](https://mlsanity.com/paper/2502.10862/) (2025)
- [Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution](https://mlsanity.com/paper/2309.12529/) (2023)
- [Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design](https://mlsanity.com/paper/2311.00462/) (2023)
- [Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigation](https://mlsanity.com/paper/2403.14583/) (2024)
- [Evolution and learning in differentiable robots](https://mlsanity.com/paper/2405.14712/) (2024)
- [Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming](https://mlsanity.com/paper/2206.06674/) (2022)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Biological Robots: Perspectives on an Emerging Interdisciplinary Field](https://mlsanity.com/paper/2207.00880/) (2022)
- [Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites](https://mlsanity.com/paper/2504.06523/) (2025)
- [Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization](https://mlsanity.com/paper/2603.15388/) (2026)
- [Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments](https://mlsanity.com/paper/2403.19545/) (2024)
- [Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation](https://mlsanity.com/paper/2404.14625/) (2024)
- [Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better](https://mlsanity.com/paper/2309.13099/) (2023)
- [No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots](https://mlsanity.com/paper/2407.16613/) (2024)
- [Efficient and Diverse Generative Robot Designs using Evolution and Intrinsic Motivation](https://mlsanity.com/paper/2411.18423/) (2024)
- [Fairness in Reinforcement Learning with Bisimulation Metrics](https://mlsanity.com/paper/2412.17123/) (2024)

## Abstract (arXiv)

Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition.