MorphoGen: Evolving Robot Morphologies with Large Language Models

ICLR 2026 Conference Submission12277 Authors

Keywords: Robot Morphology Optimization, Large Language Models, Evolutionary Algorithms

Abstract: Designing high-performing robot morphologies is a grand challenge for developing specialized autonomous agents. However, the vast, combinatorial, and non-differentiable nature of the morphological design space has been a primary obstacle. Existing methods tackle this problem indirectly, relying on either semantically-blind genetic operators or reinforcement learning with predefined modification actions, both of which constrain exploration. In this work, we introduce MorphoGen, a novel framework that reframes morphological design as a code generation problem. MorphoGen leverages large language models (LLMs) to directly iterate the XML files as codes that define an agent’s morphology, solving the original open problem without being limited by any prior constraints or fixed action spaces. Gradient-like textual guidance is provided to steer the evolution of robot morphologies through prompted mutations and crossovers. Our approach allows the LLMs to apply its understanding of structure and syntax to generate complex and semantically coherent design variations, enabling an unconstrained and efficient exploration of the design space. On a suite of challenging locomotion benchmarks, MorphoGen discovers novel and high-performing morphologies, significantly outperforming strong baselines by over 52.9% in downstream motoring evaluation. Our work unlocks a new paradigm for automated robotic design, demonstrating the effectiveness of LLMs in navigating complex, structured engineering search spaces. Codes for our work are released anonymously at https://anonymous.4open.science/r/MorphoGen-ACC/

Primary Area: applications to robotics, autonomy, planning

Submission Number: 12277