Human Data is confronting significant challenges in effectively embodying intelligence, prompting the development of a Data Foundation Model (DFM). This innovative framework aims to convert raw human data into high-quality, multi-modal, and task-ready formats, thereby enhancing data accuracy, efficiency, and scalability for training embodied models. The DFM is designed to provide a robust infrastructure that facilitates data integration and understanding while allowing for continuous evolution. By addressing these critical issues, the DFM seeks to improve the overall effectiveness of data utilization in various applications.
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Original from leaderobot.com: Why Human Data Requires a Data Foundation Model
Why Human Data Requires a Data Foundation Model
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BioRobotics Laboratory, Korea University of Technology and Education
Korea, Republic of (South)
South Korean university research laboratory focusing on haptics, exoskeletons, rehabilitation robotics, and autonomous ground vehicles.
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United States
VA–Univ. of Pittsburgh research center founded 1994, developing wheelchair robotics and assistive mobility tech for veterans and people with disabilities.
Robotics Education & Competition Foundation
United States
The REC Foundation is a US nonprofit that runs VEX-based robotics and drone competitions reaching 1.1 million students across 70 countries.
Datanomix
United States
No-Operator-Input production monitoring software that turns CNC machine data into real-time visibility and prescriptive coaching for manufacturers.
Data Science Automation, Inc.
United States
Pittsburgh-area automation systems integrator since 1993; NI Select Partner; 3,000+ solutions across manufacturing, R&D, government, and test automation.
Anyscale
United States
Anyscale is a San Francisco AI infrastructure firm behind Ray, the open-source engine for large-scale model training; Nscale agreed to buy it for $1.65B.
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