This book constitutes the refereed proceedings of the 4th International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2014, held in Bergamo, Italy, in October 2014.;The 49 revised f...
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ISBN:
(数字)9783319119007
ISBN:
(纸本)9783319118994
This book constitutes the refereed proceedings of the 4th International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2014, held in Bergamo, Italy, in October 2014.;The 49 revised full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on simulation, modeling, programming, architectures, methods and tools, and systems and applications.
Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs. In robotics, active learning allows a robot to adap...
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Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs. In robotics, active learning allows a robot to adapt its perception intelligence to a new environment with users’ help. This paper presents a new active learning method for elderly care robots to select data that is not only useful for learning but also easy for the elderly user to label. First, a series of image properties related to annotation difficulty are determined based on existing medical researches in human vision in elderly population. Based on that, a user study is conducted to determine the ground truth of annotation difficulty of images for the older adults. Second, a robust annotation difficulty predictor is developed using the results of the user study, and the difficulty prediction of an image is combined with three other active learning criteria to form an annotation difficulty-aware active learning metric, which facilitates the query data selection as the robot adapts its perception intelligence in a home environment. Third, we present an ablation study of the proposed active learning method through a simulation experiment. The experimental results validate the advantages of the proposed method.
This book contains the latest computational intelligence methodologies and applications. This book is a collection of selected papers presented at International Conference on Sustainable Computing and Intelligent Syst...
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ISBN:
(数字)9789811622489
ISBN:
(纸本)9789811622472;9789811622502
This book contains the latest computational intelligence methodologies and applications. This book is a collection of selected papers presented at International Conference on Sustainable Computing and Intelligent Systems (SCIS 2021), held in Jaipur, India, during February 5–6, 2021. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers selected papers in the area of artificialintelligence and intelligent systems, intelligent business systems, machine intelligence, computer vision, Web intelligence, big data analytics, swarm intelligence, and related topics.
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and...
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ISBN:
(数字)9783031337642
ISBN:
(纸本)9783031337635;9783031337666
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.
The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides...
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The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximityto humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches.
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