Tactile Sensing Skill Learning And Robotic Dexterous Manipulation
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Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects' property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning. - Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation - Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control - Introduces recent work on human's dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration - Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches
Tactile Robotics
Tactile Robotics structures and unifies the information processing of tactile data—not only for extracting object property but also for controller computation. This book systematically introduces tactile sensors, perception, and control, providing readers with no prior background with a better sense and knowledge of robotics and machine learning and helping users understand the concept of tactile robots and their various applications for use in real-world scenarios. - Covers basic concepts in robotics and machine learning - Includes essential knowledge for robotic manipulation tasks when tactile information is required - Employs numerous applications to illustrate how tactile robotics can be used in real robotic manipulation tasks - Defines how to structure the knowledge that can be extracted from raw tactile data
Towards Autonomous Robotic Systems
This two-volume set, LNAI 15051-15052, constitutes the refereed proceedings from the 25th Annual Conference Towards Autonomous Robotic Systems, TAROS 2024, held in London, UK, during August 21-23, 2024. The 54 full papers and 11 short papers presented in these volumes were carefully reviewed and selected from 69 submissions. The papers presented in these two volumes are organized in the following topical sections: - Part I: Robotic Learning, Mapping and Planning; Robotic Modeling, Sensing and Control; Machine Vision. Part II: Human-Robot Interaction/Collaboration; Locomotion and Manipulation; Mechanism Design; Soft Robotics; Swarms and Multi-Agent Systems.