Robotics

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— Artificial Intelligence: A Modern Approach by Stuart Russell (2021)

— Pattern Recognition & Machine Learning by Christopher Bishop (2006)

— Machine Learning: A Probabilistic Perspective by Kevin Murphy (2012) [ebook]

— Deep Learning by Ian Goodfellow (2016) [ebook]

— Reinforcement Learning: An Introduction by Richard Sutton (2018)

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— Machine Learning by Tom Mitchell (1997)

— Pattern Classification by Richard Duda (2000) [ebook]

— Bayesian Reasoning & Machine Learning by David Barber (2012) [ebook]

— The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Hastie & Tibshirani & Friedman (2009) [ebook]

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— Computer Vision: A Modern Approach by Forsyth & Ponce (2012)

— Computer Vision: Algorithms and Applications [ebook only] by Richard Szeliski (2022)

— Multiple View Geometry in Computer Vision by Hartley & Zisserman (2003) [ebook]

— Digital Image Processing by Gonzalez & Woods (2018)

— Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library by Kaehler & Bradski (2016) [ebook]

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— Modern Control Systems by Richard Dorf (2011)

— Feedback Systems: An Introduction for Scientists and Engineers by Karl Åström (2008)

— Control Systems Engineering by Norman Nise (2011)

— Optimal Control Theory: An Introduction by Donald Kirk (2004) [ebook]

Many books on robotics and related disciplines are available as ebooks through U-M Library's Catalog.

In addition, the library subscribes to several specific ebook collections that might be of interest:

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Includes such titles as: 

-Key Digital Trends in Artificial Intelligence and Robotics

-Network-Based Control of Unmanned Marine Vehicles

-Advances in Swarm Intelligence

-Robotics in Natural Settings

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 Includes such books as:

-Control Theory: A Guided Tour

-Control of Mechatronic Systems

-Flexible Robot Manipulators: Modelling, Simulation and Control

-Imaging and Sensing for Unmanned Aircraft Systems

-Signal Processing to Drive Human-Computer Interaction