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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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  • Title Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
  • Author(s) Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
  • Publisher: Springer; 1st ed. (July 22, 2020); eBook (Open Access/Creative Commons Edition)
  • License(s): CC BY 4.0
  • Hardcover 154 pages
  • eBook PDF (138 pages) and ePub
  • Language: English
  • ISBN-10: 9811562628
  • ISBN-13: 978-9811562624
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Book Description

This open access book is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven context, which can be easily integrated into various robotic systems.

It introduces a fast, accurate, robot anomaly monitoring, diagnosis and recovery scheme for endowing robots with longer-term autonomy and a safer collaborative environment.

It demonstrates two robots that perform three manipulation tasks: an HIRO-NX robot that performs electronic assembly, and a Baxter robot that performs a pick-and-place task and kitting experiment, providing comprehensive guidance for professional researchers and college students

About the Authors
  • Dr. Xuefeng Zhou is an Associate Professor and Leader of the Robotics Team at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science.
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