Robust Artificial Intelligence For Neurorobotics
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Robust Artificial Intelligence For Neurorobotics
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Author : Subramanian Ramamoorthy
language : en
Publisher: Frontiers Media SA
Release Date : 2022-01-31
Robust Artificial Intelligence For Neurorobotics written by Subramanian Ramamoorthy and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-31 with Science categories.
Neurorobotics Explores Machine Learning
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Author : Fei Chen
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-20
Neurorobotics Explores Machine Learning written by Fei Chen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Science categories.
Advances In Robots Trajectories Learning Via Fast Neural Networks
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Author : Jose De Jesus Rubio
language : en
Publisher: Frontiers Media SA
Release Date : 2021-05-14
Advances In Robots Trajectories Learning Via Fast Neural Networks written by Jose De Jesus Rubio and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-14 with Science categories.
Toward Learning Robots
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Author : Walter Van de Velde
language : en
Publisher: MIT Press
Release Date : 1993
Toward Learning Robots written by Walter Van de Velde and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Computers categories.
The contributions in Toward Learning Robots address the question of how a robot can be designed to acquire autonomously whatever it needs to realize adequate behavior in a complex environment. In-depth discussions of issues, techniques, and experiments in machine learning focus on improving ease of programming and enhancing robustness in unpredictable and changing environments, given limitations of time and resources available to researchers. The authors show practical progress toward a useful set of abstractions and techniques to describe and automate various aspects of learning in autonomous systems. The close interaction of such a system with the world reveals opportunities for new architectures and learning scenarios and for grounding symbolic representations, though such thorny problems as noise, choice of language, abstraction level of representation, and operationality have to be faced head-on. Contents Introduction: Toward Learning Robots * Learning Reliable Manipulation Strategies without Initial Physical Models * Learning by an Autonomous Agent in the Pushing Domain * A Cost-Sensitive Machine Learning Method for the Approach and Recognize Task * A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy of Spatial Representations * Understanding Object Motion: Recognition, Learning and Spatiotemporal Reasoning * Learning How to Plan * Robo-Soar: An Integration of External Interaction, Planning, and Learning Using Soar * Foundations of Learning in Autonomous Agents * Prior Knowledge and Autonomous Learning
Robust Latent Feature Learning For Incomplete Big Data
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Author : Di Wu
language : en
Publisher: Springer Nature
Release Date : 2022-12-06
Robust Latent Feature Learning For Incomplete Big Data written by Di Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
Applications In Neurorobotics
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Author : Corey Michael Thibeault
language : en
Publisher:
Release Date : 2012
Applications In Neurorobotics written by Corey Michael Thibeault and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Electronic books categories.
The field of neurorobotics is still in its infancy; however, its intersecting motivations are not. On the one hand, theories of neuroscience that require immersion in the real-world can be embedded in mobile agents creating complex patterns of activity believed to be a requirement for understanding higher-order neural function. On the other, the cognitive capabilities of humans remain unparalleled by artificial agents. Emulating biology is one strategy for creating more capable artificial intelligence. Despite these strong motivations for creating neurorobotic entities technological hurdles still remain at all levels. This thesis presents two different contributions to the field of neurorobotics. The first is aimed at reducing the complexity of coupling spiking neural models with virtual agents. This is accomplished through a set of tools that act to abstract the neuroscience details from roboticists and the mechanical details away from the neuroscientists. The second contribution provides an example of how higher-level cognitive theories of speech processing can be integrated into the neurorobotics paradigm. Extracting the emotional content of a speaker, independent of what is being spoken, is a daily act for most people. The neural basis for this ability remains illusive, however cognitive models have been realized. This class of models can be integrated with the biologically realistic neural simulations in a complementary way to expand the capabilities of a neurorobotic system.
Advances In Robot Learning
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Author : Jeremy Wyatt
language : en
Publisher: Springer
Release Date : 2003-06-29
Advances In Robot Learning written by Jeremy Wyatt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.
Inductive Biases In Machine Learning For Robotics And Control
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Author : Michael Lutter
language : en
Publisher: Springer Nature
Release Date : 2023-07-31
Inductive Biases In Machine Learning For Robotics And Control written by Michael Lutter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Technology & Engineering categories.
One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.
On Using Formal Methods For Safe And Robust Robot Autonomy
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Author : Karen Yan Ming Leung
language : en
Publisher:
Release Date : 2021
On Using Formal Methods For Safe And Robust Robot Autonomy written by Karen Yan Ming Leung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Artificial intelligence categories.
Advances in the fields of artificial intelligence and machine learning have unlocked a new generation of robotic systems---"learning-enabled" robots that are designed to operate in unstructured, uncertain, and unforgiving environments, especially settings where robots are required to interact in close proximity with humans. However, as learning-enabled methods, especially "deep" learning, continue to become more pervasive throughout the autonomy stack, it also becomes increasingly difficult to ascertain the performance and safety of these robotic systems and explain their behavior, necessary prerequisites for their deployment in safety-critical settings. This dissertation develops methods drawing upon techniques from the field of formal methods, namely Hamilton-Jacobi (HJ) reachability and Signal Temporal Logic (STL), to complement a learning-enabled robot autonomy stack, thereby leading to safer and more robust robot behavior. The first part of this dissertation investigates the problem of providing safety assurance for human-robot interactions, safety-critical settings wherein robots must reason about the uncertainty in human behavior to achieve seamless interactions with humans. Specifically, we develop a two-step approach where we first develop a learning-based human behavior prediction model tailored towards proactive robot planning and decision-making, which we then couple with a reachability-based safety controller that minimally intervenes whenever the robot is near safety violation. The approach is validated through human-in-the-loop simulation as well as on an experimental vehicle platform, demonstrating clear connections between theory and practice. The second part of this dissertation examines the use of STL as a formal language to incorporate logical reasoning into robot learning. In particular, we develop a technique, named STLCG, that casts STL into the same computational language as deep neural networks. Consequently, by using STLCG to express designers' domain expertise into a form compatible with neural networks, we can embed domain knowledge into learned components within the autonomy stack to provide additional levels of robustness and interpretability.
Living Machines
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Author : Tony J. Prescott
language : en
Publisher: Oxford University Press
Release Date : 2018
Living Machines written by Tony J. Prescott and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Medical categories.
Contemporary research in the field of robotics attempts to harness the versatility and sustainability of living organisms with the hope of rendering a renewable, adaptable, and robust class of technology that can facilitate self-repairing, social, and moral-even conscious-machines. This landmark volume surveys this flourishing area of research.