# Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

arXiv: https://arxiv.org/abs/2201.09863 · HTML: https://mlsanity.com/paper/2201.09863/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 24 Jan 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics](https://mlsanity.com/paper/2104.03311/) (2021)
- [Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity](https://mlsanity.com/paper/1902.05546/) (2019)
- [Reinforcement Learning for Improving Agent Design](https://mlsanity.com/paper/1810.03779/) (2018)
- [Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.01432/) (2018)
- [Neural Graph Evolution: Towards Efficient Automatic Robot Design](https://mlsanity.com/paper/1906.05370/) (2019)
- [Data-efficient Learning of Morphology and Controller for a Microrobot](https://mlsanity.com/paper/1905.01334/) (2019)
- [Task-Agnostic Morphology Evolution](https://mlsanity.com/paper/2102.13100/) (2021)
- [Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions](https://mlsanity.com/paper/1711.06605/) (2017)
- [Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA](https://mlsanity.com/paper/1911.10274/) (2019)

## Built on by (most cited first, 30 of 47)

- [DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models](https://mlsanity.com/paper/2311.17053/) (2023)
- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments](https://mlsanity.com/paper/2303.09555/) (2023)
- [GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation](https://mlsanity.com/paper/2209.11748/) (2022)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [BodyGen: Advancing Towards Efficient Embodiment Co-Design](https://mlsanity.com/paper/2503.00533/) (2025)
- [Learning to Design and Use Tools for Robotic Manipulation](https://mlsanity.com/paper/2311.00754/) (2023)
- [DittoGym: Learning to Control Soft Shape-Shifting Robots](https://mlsanity.com/paper/2401.13231/) (2024)
- [Co-evolving morphology and control of soft robots using a single genome](https://mlsanity.com/paper/2212.11517/) (2022)
- [Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design](https://mlsanity.com/paper/2311.00462/) (2023)
- [Evolution and learning in differentiable robots](https://mlsanity.com/paper/2405.14712/) (2024)
- [RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward](https://mlsanity.com/paper/2506.00276/) (2025)
- [A Unified Substrate for Body-Brain Co-evolution](https://mlsanity.com/paper/2203.12066/) (2022)
- [Task2Morph: Differentiable Task-inspired Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2403.19093/) (2024)
- [HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling](https://mlsanity.com/paper/2408.01230/) (2024)
- [Natural Selection via Foundation Models for Soft Robot Evolution](https://mlsanity.com/paper/2503.02249/) (2025)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems](https://mlsanity.com/paper/2406.19825/) (2024)
- [Structural Optimization of Lightweight Bipedal Robot via SERL](https://mlsanity.com/paper/2408.15632/) (2024)
- [Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites](https://mlsanity.com/paper/2504.06523/) (2025)
- [D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping](https://mlsanity.com/paper/2603.01151/) (2026)
- [ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents](https://mlsanity.com/paper/2309.04085/) (2023)
- [A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies](https://mlsanity.com/paper/2309.13908/) (2023)
- [Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation](https://mlsanity.com/paper/2404.14625/) (2024)
- [Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery](https://mlsanity.com/paper/2510.22336/) (2025)
- [Co-design of Embodied Neural Intelligence via Constrained Evolution](https://mlsanity.com/paper/2205.10688/) (2022)
- [No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots](https://mlsanity.com/paper/2407.16613/) (2024)
- [Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation](https://mlsanity.com/paper/2602.17921/) (2026)
- [Evolving Embodied Intelligence: Graph Neural Network--Driven Co-Design of Morphology and Control in Soft Robotics](https://mlsanity.com/paper/2603.19582/) (2026)

## Abstract (arXiv)

Both the design and control of a robot play equally important roles in its task performance. However, while optimal control is well studied in the machine learning and robotics community, less attention is placed on finding the optimal robot design. This is mainly because co-optimizing design and control in robotics is characterized as a challenging problem, and more importantly, a comprehensive evaluation benchmark for co-optimization does not exist. In this paper, we propose Evolution Gym, the first large-scale benchmark for co-optimizing the design and control of soft robots. In our benchmark, each robot is composed of different types of voxels (e.g., soft, rigid, actuators), resulting in a modular and expressive robot design space. Our benchmark environments span a wide range of tasks, including locomotion on various types of terrains and manipulation. Furthermore, we develop several robot co-evolution algorithms by combining state-of-the-art design optimization methods and deep reinforcement learning techniques. Evaluating the algorithms on our benchmark platform, we observe robots exhibiting increasingly complex behaviors as evolution progresses, with the best evolved designs solving many of our proposed tasks. Additionally, even though robot designs are evolved autonomously from scratch without prior knowledge, they often grow to resemble existing natural creatures while outperforming hand-designed robots. Nevertheless, all tested algorithms fail to find robots that succeed in our hardest environments. This suggests that more advanced algorithms are required to explore the high-dimensional design space and evolve increasingly intelligent robots -- an area of research in which we hope Evolution Gym will accelerate progress. Our website with code, environments, documentation, and tutorials is available at http://evogym.csail.mit.edu.