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Cloth. Condizione: Near Fine. 503 pp. Tightly bound. Corners not bumped. Text is free of markings. No ownership markings.

Hardcover. Condizione: Very Good. pictorial hardcover without dust jacket, tight, pages clear and bright, shelf and edge wear, corners bumped, packaged in cardboard box for shipment, tracking on U.S. orders.

Condizione: Very Good.

Condizione: very good. New York & Oxford : Oxford University Press, 1997, Hardcover. Viii, 355p : ill ; 27cm. Companion volume to: Early visual learning. Includes bibliographical references and index. - Some of the fundamental constraints of automated machine vision have been the inability to automatically adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as a companion volume to Early Visual Learning (edited by S. Nayar and T. Poggio), this book is intended for researchers and students in machine vision and machine learning. Condition : very good copy. ISBN 9780195098709. Keywords : PSYCHOLOGY,

Condizione: New. pp. 540 Illus.

Condizione: New. pp. 540.

Hardcover. Condizione: Brand New. 220 pages. 9.45x6.62x9.69 inches. In Stock.

Hardcover. Condizione: new. Hardcover. This book presents recent breakthroughs in the field of Learning-from-Observation (LfO) resulting from advancement in large language models (LLM) and reinforcement learning (RL) and positions it in the context of historical developments in the area. LfO involves observing human behaviors and generating robot actions that mimic these behaviors. While LfO may appear similar, on the surface, to Imitation Learning (IL) in the machine learning community and Programing-by-Demonstration (PbD) in the robotics community, a significant difference lies in the fact that these methods directly imitate human hand movements, whereas LfO encodes human behaviors into the abstract representations and then maps these representations onto the currently available hardware (individual body) of the robot, thus indirectly mimicking them. This indirect imitation allows for absorbing changes in the surrounding environment and differences in robot hardware. Additionally, by passing through this abstract representation, filtering can occur, distinguishing between important and less important aspects of human behavior, enabling imitation with fewer demonstrations and less demanding demonstrations. The authors have been researching the LfO paradigm for the past decade or so. Previously, the focus was primarily on designing necessary and sufficient task representations to define specific task domains such as assembly of machine parts, knot-tying, and human dance movements. Recent advancements in Generative Pre-trained Transformers (GPT) and RL have led to groundbreaking developments in methods to obtain and map these abstract representations. By utilizing GPT, the authors can automatically generate abstract representations from videos, and by employing RL-trained agent libraries, implementing robot actions becomes more feasible. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New. pp. 540.

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Hardcover. Condizione: Brand New. 220 pages. 9.45x6.62x9.69 inches. In Stock.

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Hardcover. Condizione: new. Hardcover. This book presents recent breakthroughs in the field of Learning-from-Observation (LfO) resulting from advancement in large language models (LLM) and reinforcement learning (RL) and positions it in the context of historical developments in the area. LfO involves observing human behaviors and generating robot actions that mimic these behaviors. While LfO may appear similar, on the surface, to Imitation Learning (IL) in the machine learning community and Programing-by-Demonstration (PbD) in the robotics community, a significant difference lies in the fact that these methods directly imitate human hand movements, whereas LfO encodes human behaviors into the abstract representations and then maps these representations onto the currently available hardware (individual body) of the robot, thus indirectly mimicking them. This indirect imitation allows for absorbing changes in the surrounding environment and differences in robot hardware. Additionally, by passing through this abstract representation, filtering can occur, distinguishing between important and less important aspects of human behavior, enabling imitation with fewer demonstrations and less demanding demonstrations. The authors have been researching the LfO paradigm for the past decade or so. Previously, the focus was primarily on designing necessary and sufficient task representations to define specific task domains such as assembly of machine parts, knot-tying, and human dance movements. Recent advancements in Generative Pre-trained Transformers (GPT) and RL have led to groundbreaking developments in methods to obtain and map these abstract representations. By utilizing GPT, the authors can automatically generate abstract representations from videos, and by employing RL-trained agent libraries, implementing robot actions becomes more feasible. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent breakthroughs in the field of Learning-from-Observation (LfO) resulting from advancement in large language models (LLM) and reinforcement learning (RL) and positions it in the context of historical developments in the area. LfO involves observing human behaviors and generating robot actions that mimic these behaviors. While LfO may appear similar, on the surface, to Imitation Learning (IL) in the machine learning community and Programing-by-Demonstration (PbD) in the robotics community, a significant difference lies in the fact that these methods directly imitate human hand movements, whereas LfO encodes human behaviors into the abstract representations and then maps these representations onto the currently available hardware (individual body) of the robot, thus indirectly mimicking them. This indirect imitation allows for absorbing changes in the surrounding environment and differences in robot hardware. Additionally, by passing through this abstract representation, filtering can occur, distinguishing between important and less important aspects of human behavior, enabling imitation with fewer demonstrations and less demanding demonstrations. The authors have been researching the LfO paradigm for the past decade or so. Previously, the focus was primarily on designing necessary and sufficient task representations to define specific task domains such as assembly of machine parts, knot-tying, and human dance movements. Recent advancements in Generative Pre-trained Transformers (GPT) and RL have led to groundbreaking developments in methods to obtain and map these abstract representations. By utilizing GPT, the authors can automatically generate abstract representations from videos, and by employing RL-trained agent libraries, implementing robot actions becomes more feasible.

Hardcover. Condizione: new. Hardcover. This book presents recent breakthroughs in the field of Learning-from-Observation (LfO) resulting from advancement in large language models (LLM) and reinforcement learning (RL) and positions it in the context of historical developments in the area. LfO involves observing human behaviors and generating robot actions that mimic these behaviors. While LfO may appear similar, on the surface, to Imitation Learning (IL) in the machine learning community and Programing-by-Demonstration (PbD) in the robotics community, a significant difference lies in the fact that these methods directly imitate human hand movements, whereas LfO encodes human behaviors into the abstract representations and then maps these representations onto the currently available hardware (individual body) of the robot, thus indirectly mimicking them. This indirect imitation allows for absorbing changes in the surrounding environment and differences in robot hardware. Additionally, by passing through this abstract representation, filtering can occur, distinguishing between important and less important aspects of human behavior, enabling imitation with fewer demonstrations and less demanding demonstrations. The authors have been researching the LfO paradigm for the past decade or so. Previously, the focus was primarily on designing necessary and sufficient task representations to define specific task domains such as assembly of machine parts, knot-tying, and human dance movements. Recent advancements in Generative Pre-trained Transformers (GPT) and RL have led to groundbreaking developments in methods to obtain and map these abstract representations. By utilizing GPT, the authors can automatically generate abstract representations from videos, and by employing RL-trained agent libraries, implementing robot actions becomes more feasible. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In.

Condizione: New.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 368 | Sprache: Englisch | Produktart: Bücher | Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning, as presented in this book, consists of an area of research that tries to overcome these fundamental constraints, enhancing state-of-the-art recognition systems that can measure their own performance, learn from their experience, and outperform conventional static designs. It was written as a companion volume to Early Visual Learning edited by S. Nayar and T. Poggio.

Condizione: New.

Condizione: New. pp. 236.

Condizione: As New. Unread book in perfect condition.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 504 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.