In the era of driven artificial intelligence, the demand for high-quality data by large language models (LLMs) like GPT-3 and BERT is increasing. However, manually sorting this data from the web is not only time-consuming and labor-intensive but also often difficult to scale. This poses significant challenges for developers, especially when large amounts of data are required. Traditional web crawlers and data scraping tools have limited capabilities in extracting structured data; although they can collect webpage data, they often cannot format it in a style suitable for LLM processing.

Researchers from Stanford University have proposed a low-cost holistic teleoperation system named Mobile ALOHA for collecting data on holistic teleoperation. Mobile ALOHA extends the capabilities of the original ALOHA by placing it on a wheeled base, giving it mobility. The researchers utilized a static ALOHA dataset for imitation learning, achieving good performance in mobile operation tasks through pre-training and joint training. This study addresses tasks that require holistic teleoperation on a daily basis.