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Rethinking Networked Robotics in the 6G Era With Generative AI-in-the-Loop

Authors: Peizheng Li; Xinyi Lin; Adnan Aijaz;

Rethinking Networked Robotics in the 6G Era With Generative AI-in-the-Loop

Abstract

Networked robotics continues to be a key component of various industrial systems. 6G will empower networked robotics with the envisaged capabilities like hyper-reliable and low-latency connectivity and in-network intelligence. While communication and control techniques have been widely investigated; co-design and AI-based solutions for autonomy and reasoning are still emerging. In this article, we propose a generative AI-in-the-Loop (GITL) framework for the control and coordination of networked robotic systems. The generative AI (GAI) agent oversees three interrelated loops: the communication loop, the robot control loop, and the AI model control loop, making holistic decisions by interpreting task requirements, network status, and robotic operations. Acting as a high-level cross-domain coordinator, the GAI agent leverages background knowledge of AI models, robot behaviors, and network policies, ensuring seamless interplay across different operational scenarios. This approach ultimately enables a highly autonomous networked robotic system capable of handling complex tasks with minimal human intervention. We anticipate that the GITL paradigm will play a pivotal role in unlocking the full potential of networked robotic systems, enabling enhanced autonomy, adaptability, and coordination in the emerging 6G and Industry 5.0 era.

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Keywords

Robots , Robot kinematics , Artificial intelligence , Gas insulated transmission lines , Adaptation models , 6G mobile communication , Service robots , Robot sensing systems , Real-time systems , Robot control

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green