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Annals of Scientific Society for Assembly, Handling and Industrial Robotics

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ISBN: 9783662617557 Year: Pages: 344 DOI: 10.1007/978-3-662-61755-7 Language: English
Publisher: Springer Nature
Subject: Agriculture (General) --- Computer Science
Added to DOAB on : 2020-09-01 00:02:13
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This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI).

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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ISBN: 9789811562631 Year: Pages: 137 DOI: 10.1007/978-981-15-6263-1 Language: English
Publisher: Springer Nature
Subject: Agriculture (General) --- Mathematics --- Computer Science
Added to DOAB on : 2020-09-01 00:02:34
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This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.

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