Large language models help build clearer robot design models
Large Language Models for Model-Based Robot Design
Robotics
Summary
Designing robots often involves complex assumptions that affect which designs are actually possible or best. The authors show that large language models can help create detailed engineering models that make these assumptions clear and open to change. This allows engineers to check and update the design rules before running formal tests to find the best designs. Their tests on quadcopters and simple robots showed that direct designs from language models often didn’t meet feasibility checks without this extra modeling step.
What this means in practice
- •For robot design teams: Use language models to generate clear and editable design models that can be formally optimized for better robot component choices.
- •For mechanical engineers: Improve component selection processes by combining language model knowledge with detailed engineering constraints before optimization.
Authors
Andrew Wilhelm, Angelina Zhao, Nils Napp
Abstract
Large Language Models (LLMs) can contribute useful engineering knowledge to robot design, but directly generated designs may rely on implicit assumptions and provide no guarantees of feasibility or optimality. These assumptions are critical because different reasonable modeling choices can materially change which designs are predicted to be feasible or optimal. We therefore present a framework that uses LLMs to construct explicit engineering models containing physical relationships, compatibility constraints, and objectives, allowing these modeling choices to be inspected and revised before formal optimization. The model can then be updated with additional engineering, manufacturer, or system-specific information before formal multi-objective optimization provides feasibility and Pareto-optimality guarantees with respect to the finalized model and specified design space. We evaluate the framework on quadcopter and line-following robot component-selection problems. Across 30 direct LLM design trials, none could be verified as feasible under the corresponding finalized model. Comparisons with an independently developed expert model and successive stages of model refinement further showed that changes in modeling assumptions substantially altered the predicted feasible and Pareto-optimal design sets. Together, these results show that using LLMs to construct explicit engineering models makes the underlying design choices available for inspection and revision before those assumptions determine the optimized designs. Explicit modeling therefore provides an interface for combining LLM-generated engineering knowledge, system-specific information, and formal design optimization.