Large Language Models as Natural Selector for Embodied Soft Robot Design

2025-03-04

AI Summary

This paper addresses the semantic gap between high-level task specifications and low-level morphological/material selections in soft robot design by proposing a novel paradigm that leverages large language models (LLMs) as embodied design representation learners. Methodologically, we introduce RoboCrafter-QA—the first question-answering benchmark for soft robot design—built upon EvoGym-based multi-task simulations to generate task-morphology-material mappings, and systematically evaluate performance using multimodal LLMs integrated with an embodied AI assessment framework. Key contributions include: (1) the first empirical evaluation of LLMs’ capacity to acquire embodied design knowledge; (2) evidence that state-of-the-art multimodal LLMs capture coarse-grained design principles but struggle to distinguish fine-grained performance differences; and (3) validation of LLMs’ efficacy in early-stage design initialization, establishing a new pathway toward embodied-intelligence-driven automated design.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Designing soft robots is a complex and iterative process that demands cross-disciplinary expertise in materials science, mechanics, and control, often relying on intuition and extensive experimentation. While Large Language Models (LLMs) have demonstrated impressive reasoning abilities, their capacity to learn and apply embodied design principles--crucial for creating functional robotic systems--remains largely unexplored. This paper introduces RoboCrafter-QA, a novel benchmark to evaluate whether LLMs can learn representations of soft robot designs that effectively bridge the gap between high-level task descriptions and low-level morphological and material choices. RoboCrafter-QA leverages the EvoGym simulator to generate a diverse set of soft robot design challenges, spanning robotic locomotion, manipulation, and balancing tasks. Our experiments with state-of-the-art multi-modal LLMs reveal that while these models exhibit promising capabilities in learning design representations, they struggle with fine-grained distinctions between designs with subtle performance differences. We further demonstrate the practical utility of LLMs for robot design initialization. Our code and benchmark will be available to encourage the community to foster this exciting research direction.

Problem

• Evaluating LLMs' ability to learn soft robot design representations.
• Bridging high-level tasks with low-level morphological/material choices.
• Assessing LLMs' utility in robot design initialization.

Innovation

• LLMs learn soft robot design representations
• RoboCrafter-QA benchmark evaluates LLM capabilities
• EvoGym simulator generates diverse robot challenges