Nonparametric Bayesian Learning for Collaborative Robot Multimodal IntrospectionThis 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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algorithm anomaly class anomaly diagnoses anomaly monitoring anomaly recovery arg max autonomous Baxter robot Bayesian nonparametric chapter classifier Collaborative Robot complex task computed conjugate prior covariance detection Dirichlet distribution Dirichlet Process distribution Dynamic neural networks environment fault finite state machine forward gradient forward-backward algorithm framework Gaussian HDP-HMM hidden Markov model HMM model human collisions human-robot collaboration IEEE IEEE/RSJ international conference implementation intelligent robots kitting experiment learning log-likelihood matrix method modality motion primitive multi-modal fusion multimodal multivariate time series node Nonparametric Bayesian object observation parameters pick-and-place task Pre-pick probability proposed recovery behavior recovery policy redundant manipulators reverse execution robot anomaly robot introspection robot manipulation task robot performing robot tasks robots and systems samples sequence sequence break sHDP-VAR-HMM signals skill identification supervised learning tactile sensor task representation threshold tool collision trajectory transition vector Viterbi algorithm wrench Zhou