MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation

• URL: http://arxiv.org/abs/2508.09005v1
• Date: Tue, 12 Aug 2025 15:17:30 GMT
• Title: MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation
• Authors: Diana Bolanos, Mohammadmehdi Ataei, Pradeep Kumar Jayaraman,
• Abstract summary: We introduce MechaFormer, a Transformer-based model that tackles mechanism design as a conditional sequence generation task.<n>Our model learns to translate a target curve into a domain-specific language () string, simultaneously determining the mechanism's topology and geometric parameters.<n>We demonstrate a suite of sampling strategies that can dramatically improve solution quality and offer designers valuable flexibility.
• Score: 3.5169782367209135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing mechanical mechanisms to trace specific paths is a classic yet notoriously difficult engineering problem, characterized by a vast and complex search space of discrete topologies and continuous parameters. We introduce MechaFormer, a Transformer-based model that tackles this challenge by treating mechanism design as a conditional sequence generation task. Our model learns to translate a target curve into a domain-specific language (DSL) string, simultaneously determining the mechanism's topology and geometric parameters in a single, unified process. MechaFormer significantly outperforms existing baselines, achieving state-of-the-art path-matching accuracy and generating a wide diversity of novel and valid designs. We demonstrate a suite of sampling strategies that can dramatically improve solution quality and offer designers valuable flexibility. Furthermore, we show that the high-quality outputs from MechaFormer serve as excellent starting points for traditional optimizers, creating a hybrid approach that finds superior solutions with remarkable efficiency.

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