Education & Research Software & Algorithm Provider Human-Machine Interaction Motion & Navigation
Collecting robot training data is dirty, unglamorous work. Some AI labs are already paying XDOF to do it.

Collecting robot training data is dirty, unglamorous work. Some AI labs are already paying XDOF to do it.

Recent discussions in the field of artificial intelligence highlight a significant challenge facing the development of physical AI systems. Experts emphasize that in order for physical AI to achieve milestones comparable to those of large language models (LLMs), a critical data issue must be addressed. As of October 2023, the existing datasets are insufficient to support the complex learning and operational needs of physical AI. This gap in data could hinder progress and innovation in creating AI that can effectively interact with and navigate the physical world. Addressing this problem is essential for advancing the capabilities of physical AI, ensuring that it can perform tasks with the same proficiency as its software counterparts.

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Robotics needs a service framework.

RSF defines a common language for robot service capability, lifecycle operations, certification pathways, and service-provider networks.

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Tim Fernholz
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Tim Fernholz - Editor

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