Titlebook: In-Hand Object Localization and Control: Enabling Dexterous Manipulation with Robotic Hands; Martin Pfanne Book 2022 The Editor(s) (if app

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978-3-031-06969-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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In-Hand Object Localization and Control: Enabling Dexterous Manipulation with Robotic Hands978-3-031-06967-3Series ISSN 1610-7438 Series E-ISSN 1610-742X

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Martin PfannePresents state of the art in model-based dexterous manipulation with robotic hands.Is tested in challenging real-world manipulation scenarios, using one of the most advanced robotic hand systems.Intro

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Grasp Modeling,This work is concerned with the development of methods for the localization and control of manipulated objects. However, the discussion of the proposed algorithms first requires a common model of the hand-object system, on which both components can be built. This chapter presents the utilized grasp model.

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Grasp State Estimation,This chapter introduces the proposed method for the estimation of the grasp state of a manipulated object. It combines different sensor modalities in order to provide a robust estimate of the object pose, contact configuration and joint position errors.

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Impedance-Based Object Control,Enabled by the grasp state estimation method, the developed in-hand object controller is presented in this chapter. The impedance-based method allows the compliant positioning of a grasped object inside of the hand, while at the same time regulating the internal forces on the object.

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Conclusion,In this book, the main algorithmic components of a model-based dexterous manipulation framework were presented. Novel approaches for the grasp state estimation and in-hand object control were developed and validated in a range of real-world experiments.