A remarkable startup is developing a groundbreaking AI system that promises to give creators unprecedented capabilities for generating cinematic worlds. The company, named Odyssey, employs a unique approach: having staff carry specialized camera systems to gather scene data from around the globe. Founded by autonomous driving experts Oliver Cameron and Jeff Hawke, Odyssey recently completed an $18 million Series A funding round led by EQT Ventures.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

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.