# Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer

arXiv: https://arxiv.org/abs/2202.04575 · HTML: https://mlsanity.com/paper/2202.04575/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2022.

## Built on by (most cited first, 10 of 10)

- [DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models](https://mlsanity.com/paper/2311.17053/) (2023)
- [Efficient automatic design of robots](https://mlsanity.com/paper/2306.03263/) (2023)
- [Reinforcement learning for freeform robot design](https://mlsanity.com/paper/2310.05670/) (2023)
- [Text2Robot: Evolutionary Robot Design from Text Descriptions](https://mlsanity.com/paper/2406.19963/) (2024)
- [Evolution and learning in differentiable robots](https://mlsanity.com/paper/2405.14712/) (2024)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots](https://mlsanity.com/paper/2303.04136/) (2023)
- [Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery](https://mlsanity.com/paper/2510.22336/) (2025)
- [Solving Vision Tasks with Simple Photoreceptors Instead of Cameras](https://mlsanity.com/paper/2406.11769/) (2024)
- [RobotDesignGPT: Automated Robot Design Synthesis using Vision Language Models](https://mlsanity.com/paper/2601.11801/) (2026)

## Abstract (arXiv)

This work provides a complete framework for the simulation, co-optimization, and sim-to-real transfer of the design and control of soft legged robots. The compliance of soft robots provides a form of "mechanical intelligence" -- the ability to passively exhibit behaviors that would otherwise be difficult to program. Exploiting this capacity requires careful consideration of the coupling between mechanical design and control. Co-optimization provides a promising means to generate sophisticated soft robots by reasoning over this coupling. However, the complex nature of soft robot dynamics makes it difficult to provide a simulation environment that is both sufficiently accurate to allow for sim-to-real transfer, while also being fast enough for contemporary co-optimization algorithms. In this work, we show that finite element simulation combined with recent model order reduction techniques provide both the efficiency and the accuracy required to successfully learn effective soft robot design-control pairs that transfer to reality. We propose a reinforcement learning-based framework for co-optimization and demonstrate successful optimization, construction, and zero-shot sim-to-real transfer of several soft crawling robots. Our learned robot outperforms an expert-designed crawling robot, showing that our approach can generate novel, high-performing designs even in well-understood domains.