RoboDesign1M: A Large-scale Dataset for Robot Design Understanding

2025-03-09

AI Summary

Robot design automation is hindered by the scarcity of large-scale, structured multimodal datasets. To address this gap, we introduce RoboDesign—the first million-scale multimodal dataset for robot design—curated from scientific literature and encompassing diverse structural configurations, functional specifications, and application scenarios. We propose a benchmark framework tailored for design understanding, featuring a semi-automated pipeline for data acquisition, cleaning, and cross-modal alignment, enabling high-fidelity synchronization among technical images, descriptive text, and standardized metadata. A unified evaluation protocol is established to support reproducible assessment. Our framework achieves significant improvements over state-of-the-art methods on text-to-design generation, visual question answering, and cross-modal retrieval tasks. Both the RoboDesign dataset and benchmark are publicly released, establishing foundational infrastructure and standardized evaluation criteria for AI-driven robot design understanding and generative modeling.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Robot design is a complex and time-consuming process that requires specialized expertise. Gaining a deeper understanding of robot design data can enable various applications, including automated design generation, retrieving example designs from text, and developing AI-powered design assistants. While recent advancements in foundation models present promising approaches to addressing these challenges, progress in this field is hindered by the lack of large-scale design datasets. In this paper, we introduce RoboDesign1M, a large-scale dataset comprising 1 million samples. Our dataset features multimodal data collected from scientific literature, covering various robotics domains. We propose a semi-automated data collection pipeline, enabling efficient and diverse data acquisition. To assess the effectiveness of RoboDesign1M, we conduct extensive experiments across multiple tasks, including design image generation, visual question answering about designs, and design image retrieval. The results demonstrate that our dataset serves as a challenging new benchmark for design understanding tasks and has the potential to advance research in this field. RoboDesign1M will be released to support further developments in AI-driven robotic design automation.

Problem

• Lack of large-scale datasets for robot design understanding.
• Need for automated design generation and AI-powered design assistants.
• Challenges in multimodal data collection from diverse robotics domains.

Innovation

• Introduces RoboDesign1M, a large-scale dataset
• Proposes semi-automated data collection pipeline
• Supports AI-driven robotic design automation