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Organises and summarises the large body of research related to facilitation of safe human-robot interaction. This book describes the strategies and methods that have been developed thus far, organises them into subcategories, characterizes relationships between the strategies, and identifies potential gaps in the existing knowledge.
Familiarizes machine learning experts with imitation learning, statistical supervised learning theory, and reinforcement learning and roboticists and experts in applied AI with broader appreciation for the frameworks and tools available for imitation learning.
Factor Graphs for Robot Perception reviews the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are introduced as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them.
This monograph presents a comprehensive review of literature related to the generation and usage of nonverbal signals that facilitate legibility of non-humanoid robot state and behavior.
Presents a holistic, energy-based view of robotic systems. The book examines the relevance of such energy considerations to robotics. Using the theory of Port-Hamiltonian Systems as a fundamental basis, it provides examples pertaining to energy measurement, passivity and safety.
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