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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.

Robotics. --- Automation. --- Statistics . --- Control engineering. --- Mechatronics. --- Machine learning. --- Mathematical models. --- Robotics and Automation. --- Bayesian Inference. --- Control, Robotics, Mechatronics. --- Machine Learning. --- Mathematical Modeling and Industrial Mathematics. --- Models, Mathematical --- Simulation methods --- Learning, Machine --- Artificial intelligence --- Machine theory --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Automatic factories --- Automatic production --- Computer control --- Engineering cybernetics --- Factories --- Industrial engineering --- Mechanization --- Assembly-line methods --- Automatic control --- Automatic machinery --- CAD/CAM systems --- Robotics --- Robotics and Automation --- Bayesian Inference --- Control, Robotics, Mechatronics --- Machine Learning --- Mathematical Modeling and Industrial Mathematics --- Robotic Engineering --- Control, Robotics, Automation --- Collaborative Robot Introspection --- Nonparametric Bayesian Inference --- Anomaly Monitoring and Diagnosis --- Multimodal Perception --- Anomaly Recovery --- Human-robot Collaboration --- Robot Safety and Protection --- Hidden Markov Model --- Robot Autonomous Manipulation --- open access --- Bayesian inference --- Automatic control engineering --- Electronic devices & materials --- Machine learning --- Mathematical modelling --- Maths for engineers

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This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

Robotics. --- Automation. --- Control engineering. --- Artificial intelligence. --- Robotics and Automation. --- Control and Systems Theory. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- Automatic factories --- Automatic production --- Computer control --- Engineering cybernetics --- Factories --- Industrial engineering --- Mechanization --- Assembly-line methods --- Automatic control --- Automatic machinery --- CAD/CAM systems --- Robotics --- Robotics and Automation --- Control and Systems Theory --- Artificial Intelligence --- Robotic Engineering --- Safe Control --- Deep Reinforcement Learning --- Recurrent Neural Network --- Force Control --- Obstacle Ovoidance --- Adaptive Control --- Trajectory Tracking --- Open Access --- Automatic control engineering --- Artificial intelligence

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Statistical science --- Mathematics --- Electrical engineering --- Applied physical engineering --- Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- superclaus proces --- automatisering --- mathematische modellen --- statistiek --- programmeren (informatica) --- wiskunde --- robots --- automatische regeltechniek