# Efficient automatic design of robots

arXiv: https://arxiv.org/abs/2306.03263 · HTML: https://mlsanity.com/paper/2306.03263/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Evolved Policy Gradients](https://mlsanity.com/paper/1802.04821/) (2018)
- [Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity](https://mlsanity.com/paper/1902.05546/) (2019)
- [Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design](https://mlsanity.com/paper/2110.03659/) (2021)
- [Functional Co-Optimization of Articulated Robots](https://mlsanity.com/paper/1707.06617/) (2017)
- [Scalable sim-to-real transfer of soft robot designs](https://mlsanity.com/paper/1911.10290/) (2019)
- [Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer](https://mlsanity.com/paper/2202.04575/) (2022)
- [DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation](https://mlsanity.com/paper/2104.00837/) (2021)
- [Why Do Local Methods Solve Nonconvex Problems?](https://mlsanity.com/paper/2103.13462/) (2021)
- [Scale invariant robot behavior with fractals](https://mlsanity.com/paper/2103.04876/) (2021)

## Built on by (most cited first, 14 of 14)

- [When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges](https://mlsanity.com/paper/2401.10510/) (2024)
- [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)
- [Accelerated co-design of robots through morphological pretraining](https://mlsanity.com/paper/2502.10862/) (2025)
- [Evolution and learning in differentiable robots](https://mlsanity.com/paper/2405.14712/) (2024)
- [House of Dextra: Cross-embodied Co-design for Dexterous Hands](https://mlsanity.com/paper/2512.03743/) (2025)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [A Design Co-Pilot for Task-Tailored Manipulators](https://mlsanity.com/paper/2509.13077/) (2025)
- [Efficient and Diverse Generative Robot Designs using Evolution and Intrinsic Motivation](https://mlsanity.com/paper/2411.18423/) (2024)
- [Solving Vision Tasks with Simple Photoreceptors Instead of Cameras](https://mlsanity.com/paper/2406.11769/) (2024)
- [AI-Enhanced Automatic Design of Efficient Underwater Gliders](https://mlsanity.com/paper/2505.00222/) (2025)
- [Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design](https://mlsanity.com/paper/2609.31329/) (2026)
- [Evolving Dexterous Robots from Scratch](https://mlsanity.com/paper/2609.33101/) (2026)
- [Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization](https://mlsanity.com/paper/2610.02366/) (2026)

## Abstract (arXiv)

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.