X万字解读具身智能数据工程 | Jinqiu Select

Core Insights - The article discusses the limitations of embodied artificial intelligence (EAI) data engineering, highlighting the challenges posed by data bottlenecks in the field, particularly in cost efficiency, data silos, and evaluation vacuums [1][5][25]. - A comprehensive framework for EAI data engineering is proposed, aiming to create a systematic, standardized, and scalable data solution for embodied intelligent systems [1][8][10]. Group 1: Data Bottlenecks - The three core data bottlenecks identified are low cost efficiency, data silos, and evaluation vacuums [25][26][28]. - The current data available for EAI is significantly less than that for large language models, with only one ten-thousandth of the data volume [6][25]. - The unique nature of EAI data requires capturing the spatiotemporal relationships between agent behavior and environmental changes, complicating data acquisition [6][25]. Group 2: Current Data Production Challenges - Existing data production methods for EAI are fragmented and unsustainable, leading to inconsistencies in data quality and generalizability [7][25]. - A shift towards a systematic engineering approach is necessary to design new data production pipelines, making data engineering a foundational aspect of scalable EAI [7][10]. Group 3: EAI Data Engineering Framework - The proposed EAI data engineering framework encompasses the entire data production lifecycle, focusing on standardization to ensure high-quality, reliable multimodal datasets [8][10]. - Key components of the framework include top-level design of data production systems, establishment of unified data standards, and development of technologies for real-world data collection and simulation data generation [10][11]. Group 4: Data Collection Techniques - Real-world data collection systems are categorized into tele-operated data collection systems and teach-based data collection systems, each with distinct operational architectures [29][30]. - Tele-operated systems involve remote control of robots, while teach-based systems record human teaching actions to guide robots [29][30][33]. Group 5: Standardization of EAI Data - Standardization of EAI data is crucial for addressing data silos and evaluation vacuums, facilitating interoperability and quality assessment across diverse datasets [44][68]. - The article outlines the classification of EAI datasets, including demonstration datasets and embodied question-answering datasets, which are essential for training EAI models [45][56]. Group 6: Future Directions - The framework anticipates applications in various industries, including manufacturing and services, and emphasizes the need for continuous optimization of data quality and cost reduction [1][10].