INGRID: Intelligent Generative Robotic Design Using Large Language Models

• URL: http://arxiv.org/abs/2509.03842v3
• Date: Sun, 05 Oct 2025 14:20:32 GMT
• Title: INGRID: Intelligent Generative Robotic Design Using Large Language Models
• Authors: Guanglu Jia, Ceng Zhang, Gregory S. Chirikjian,
• Abstract summary: INGRID (Intelligent Generative Robotic Design) is a framework that enables the automated design of parallel robotic mechanisms.<n>We decompose the design challenge into four progressive tasks: constraint analysis, kinematic joint generation, chain construction, and complete mechanism design.<n>By bridging the gap between mechanism theory and machine learning, INGRID enables researchers without specialized robotics training to create custom parallel mechanisms.
• Score: 5.3909333359654275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of large language models (LLMs) into robotic systems has accelerated progress in embodied artificial intelligence, yet current approaches remain constrained by existing robotic architectures, particularly serial mechanisms. This hardware dependency fundamentally limits the scope of robotic intelligence. Here, we present INGRID (Intelligent Generative Robotic Design), a framework that enables the automated design of parallel robotic mechanisms through deep integration with reciprocal screw theory and kinematic synthesis methods. We decompose the design challenge into four progressive tasks: constraint analysis, kinematic joint generation, chain construction, and complete mechanism design. INGRID demonstrates the ability to generate novel parallel mechanisms with both fixed and variable mobility, discovering kinematic configurations not previously documented in the literature. We validate our approach through three case studies demonstrating how INGRID assists users in designing task-specific parallel robots based on desired mobility requirements. By bridging the gap between mechanism theory and machine learning, INGRID enables researchers without specialized robotics training to create custom parallel mechanisms, thereby decoupling advances in robotic intelligence from hardware constraints. This work establishes a foundation for mechanism intelligence, where AI systems actively design robotic hardware, potentially transforming the development of embodied AI systems.

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