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RoboMorph: Evolving Robot Morphology using Large Language Models

Kevin Qiu, Władysław Pałucki, Krzysztof Ciebiera, Paweł Fijałkowski, Marek Cygan, Łukasz Kuciński

TL;DR

RoboMorph tackles the challenge of automating robot morphology design by marrying large language models with a modular robot grammar, RL-based control, and evolutionary search. The method uses best-shot prompting to iteratively refine designs generated by a GPT-4o-backed pipeline, engineers XML-compatible morphologies, and evaluates them in a parallel MuJoCo-based simulation with SAC control. Across multiple terrains, RoboMorph demonstrates progressive improvements in morphology, including wheel-ended designs for flat terrain and hexapod configurations for challenging surfaces, outperforming a prior grammar-based approach in several cases. This data-driven, modular framework suggests a scalable path toward rapid, terrain-aware robot design and highlights the potential for extending LLM-driven design to other structured design domains.

Abstract

We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabilities of LLMs to navigate the extensive robot design space, which is traditionally time-consuming and computationally demanding. By introducing a best-shot prompting technique and a reinforcement learning-based control algorithm, RoboMorph iteratively improves robot designs through feedback loops. Experimental results demonstrate that RoboMorph successfully generates nontrivial robots optimized for different terrains while showcasing improvements in robot morphology over successive evolutions. Our approach highlights the potential of using LLMs for data-driven, modular robot design, providing a promising methodology that can be extended to other domains with similar design frameworks.

RoboMorph: Evolving Robot Morphology using Large Language Models

TL;DR

RoboMorph tackles the challenge of automating robot morphology design by marrying large language models with a modular robot grammar, RL-based control, and evolutionary search. The method uses best-shot prompting to iteratively refine designs generated by a GPT-4o-backed pipeline, engineers XML-compatible morphologies, and evaluates them in a parallel MuJoCo-based simulation with SAC control. Across multiple terrains, RoboMorph demonstrates progressive improvements in morphology, including wheel-ended designs for flat terrain and hexapod configurations for challenging surfaces, outperforming a prior grammar-based approach in several cases. This data-driven, modular framework suggests a scalable path toward rapid, terrain-aware robot design and highlights the potential for extending LLM-driven design to other structured design domains.

Abstract

We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabilities of LLMs to navigate the extensive robot design space, which is traditionally time-consuming and computationally demanding. By introducing a best-shot prompting technique and a reinforcement learning-based control algorithm, RoboMorph iteratively improves robot designs through feedback loops. Experimental results demonstrate that RoboMorph successfully generates nontrivial robots optimized for different terrains while showcasing improvements in robot morphology over successive evolutions. Our approach highlights the potential of using LLMs for data-driven, modular robot design, providing a promising methodology that can be extended to other domains with similar design frameworks.
Paper Structure (22 sections, 4 equations, 8 figures, 1 table, 2 algorithms)

This paper contains 22 sections, 4 equations, 8 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Design Optimization
  4. Terrains
  5. RoboMorph
  6. Prompt Structure
  7. Best-Shot Prompting
  8. Robot Design
  9. Simulation
  10. Control
  11. Evolution
  12. Experiments
  13. Experimental Setup
  14. Main Experiment
  15. RoboMorph vs. Robogrammar
  16. ...and 7 more sections

Figures (8)

  • Figure 1: Visualization of one iteration of RoboMorph. At each stage, the prompt (blue), robot design (green), and fitness score (orange) are displayed. The element modified in each step is highlighted with a red border.
  • Figure 2: Overview of the RoboMorph framework, which consists of a design stage and a control stage. The process is divided into six steps: (1) The input consists of the system prompt, user prompt, and few-shot examples. (2) Based on the input prompt, the LLM generates a new design and provides its reasoning. (3) A compiler converts the LLM-generated design into an XML file, which can be rendered in a simulator. (4) An RL control policy is trained in simulation for the robot in a given environment. (5) The robot is evaluated, and a fitness score is assigned to each design. (6) The population of robots is updated through evolution by pruning the worst-performing candidates.
  • Figure 3: Examples of initial few-shot robot designs generated using Algorithm \ref{['alg:init_few_shot']} (see Appendix \ref{['app:fewshots']}).
  • Figure 4: Maximum fitness of the best robot design in the population at each evolutionary step.
  • Figure 5: Best-performing robot designs generated by RoboMorph for each terrain: ridged (top-left), flat (top-right), frozen (bottom-left), and beams (bottom-right).
  • ...and 3 more figures