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Machine Learning Techniques for Assistive Robotics

Machine Learning Techniques for Assistive Robotics
Author: Miguel Angel Cazorla Quevedo
Publisher: MDPI
Total Pages: 210
Release: 2020-12-10
Genre: Technology & Engineering
ISBN: 3039363387
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Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.

Machine Learning Techniques for Assistive Robotics

Machine Learning Techniques for Assistive Robotics
Author: Miguel Quevedo
Publisher:
Total Pages: 210
Release: 2020
Genre:
ISBN: 9783039363391
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Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.

Assistive Technology and Artificial Intelligence

Assistive Technology and Artificial Intelligence
Author: Vibhu O. Mittal
Publisher: Springer Science & Business Media
Total Pages: 292
Release: 1998-07-15
Genre: Computers
ISBN: 9783540647904
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This book constitutes a carefully arranged selection of revised papers on assistive technology, first presented at related AAAI workshops between 1995 and 1998. The book is devoted to the advancement and use of AI stimulated technology that can help users extend their current range of cognitive and sensory abilities or overcome their motor disabilities. Among various issues in the interdisciplinary area of assistive technology, the papers address topics from natural language processing, planning, robotics, user interface design, computer vision, and learning.

Robotic Assistive Technologies

Robotic Assistive Technologies
Author: Pedro Encarnação
Publisher: CRC Press
Total Pages: 308
Release: 2017-02-03
Genre: Medical
ISBN: 1315351765
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This book contains a comprehensive overview of all current uses of robots in rehabilitation. The underlying principles in each application are provided. This is followed by a critical review of the technology available, of the utilization protocols, and of user studies, outcomes, and clinical evidence, if existing. Ethical and social implications of robot use are also discussed. The reader will have an in depth view of rehabilitation robots, from principles to practice.

Intelligent Assistive Robots

Intelligent Assistive Robots
Author: Samer Mohammed
Publisher: Springer
Total Pages: 452
Release: 2015-03-26
Genre: Technology & Engineering
ISBN: 3319129228
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This book deals with the growing challenges of using assistive robots in our everyday activities along with providing intelligent assistive services. The presented applications concern mainly healthcare and wellness such as helping elderly people, assisting dependent persons, habitat monitoring in smart environments, well-being, security, etc. These applications reveal also new challenges regarding control theory, mechanical design, mechatronics, portability, acceptability, scalability, security, etc.

Autonomous Robotics and Deep Learning

Autonomous Robotics and Deep Learning
Author: Vishnu Nath
Publisher: Springer Science & Business Media
Total Pages: 73
Release: 2014-04-11
Genre: Computers
ISBN: 3319056034
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This Springer Brief examines the combination of computer vision techniques and machine learning algorithms necessary for humanoid robots to develop “true consciousness.” It illustrates the critical first step towards reaching “deep learning,” long considered the holy grail for machine learning scientists worldwide. Using the example of the iCub, a humanoid robot which learns to solve 3D mazes, the book explores the challenges to create a robot that can perceive its own surroundings. Rather than relying solely on human programming, the robot uses physical touch to develop a neural map of its environment and learns to change the environment for its own benefit. These techniques allow the iCub to accurately solve any maze, if a solution exists, within a few iterations. With clear analysis of the iCub experiments and its results, this Springer Brief is ideal for advanced level students, researchers and professionals focused on computer vision, AI and machine learning.

Learning for Adaptive and Reactive Robot Control

Learning for Adaptive and Reactive Robot Control
Author: Aude Billard
Publisher: MIT Press
Total Pages: 425
Release: 2022-02-08
Genre: Technology & Engineering
ISBN: 0262367017
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Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Mastering Robotics

Mastering Robotics
Author:
Publisher: Cybellium Ltd
Total Pages: 111
Release:
Genre: Computers
ISBN:
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Unveil the Frontiers of Robotic Innovation and Implementation In the realm of cutting-edge technology, robotics stands as a beacon of innovation with the potential to revolutionize industries and daily life. "Mastering Robotics" is your comprehensive guide to understanding and harnessing the power of robotics—a transformative field that spans science, engineering, and creativity. About the Book: As the boundaries of human achievement expand, robotics emerges as a dynamic field with diverse applications. "Mastering Robotics" offers a deep exploration of robotics technology—a cornerstone of modern automation and innovation. This book caters to both newcomers and experienced enthusiasts seeking to excel in robotics design, development, and deployment. Key Features: Robotics Fundamentals: Begin by understanding the core principles of robotics. Learn how robots function, their components, and how they interact with the world. Robotic Kinematics and Dynamics: Dive into the mechanics of robots. Explore kinematic chains, inverse kinematics, and the principles that govern robotic motion. Sensors and Perception: Grasp the art of integrating sensors into robots. Learn how robots perceive the world through sensors and understand their surroundings. Robot Programming: Explore the intricacies of programming robots. Understand how to write code to control robots' actions, movements, and responses. Robot Vision and Machine Learning: Delve into robotic vision and machine learning. Learn how robots process visual data and adapt their behavior using advanced algorithms. Robot Localization and Mapping: Grasp the significance of localization and mapping in robotics. Understand how robots navigate and build maps of their environments. Robotic Manipulation and Control: Explore techniques for robotic manipulation and control. Learn how robots interact with objects, perform tasks, and maintain stability. Real-World Applications: Gain insights into how robotics is applied across industries. From manufacturing to healthcare, discover the diverse applications of robotic technology. Why This Book Matters: In an era of technological advancement, mastering robotics offers a transformative advantage. "Mastering Robotics" empowers engineers, researchers, and technology enthusiasts to harness the potential of robotics, enabling them to innovate and create solutions that reshape industries and redefine human capabilities. Embark on a Journey of Innovation: In the landscape of cutting-edge technology, robotics holds the promise of reshaping our world. "Mastering Robotics" equips you with the knowledge needed to unlock the potential of robotics, enabling you to design, build, and deploy robotic systems that push the boundaries of human achievement. Whether you're a seasoned professional or a newcomer to robotics, this book will guide you in building a solid foundation for innovation and exploration. Your journey to mastering robotics starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Learning from Natural Human Interactions for Assistive Robots

Learning from Natural Human Interactions for Assistive Robots
Author: Ashesh Jain
Publisher:
Total Pages: 220
Release: 2016
Genre:
ISBN:
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Leveraging human knowledge to train robots is a core problem in robotics. In the near future we will see humans interacting with agents such as, assistive robots, cars, smart houses, etc. Agents that can elicit and learn from such interactions will find use in many applications. Previous works have proposed methods for learning low-level robotic controls or motion primitives from (near) optimal human signals. In many applications such signals are not naturally available. Furthermore, optimal human signals are also difficult to elicit from non-expert users at a large scale. Understanding and learning user preferences from weak signals is therefore of great emphasis. To this end, in this dissertation we propose interactive learning systems which allow robots to learn by interacting with humans. We develop interaction methods that are natural to the end-user, and algorithms to learn from sub-optimal interactions. Furthermore, the interactions between humans and robots have complex spatio-temporal structure. Inspired by the recent success of powerful function approximators based on deep neural networks, we propose a generic framework for modeling interactions with structure of Recurrent Neural Networks. We demonstrate applications of our work on real-world scenarios on assistive robots and cars. This work also established state-of-the-art on several existing benchmarks.