[PDF] Motion Learning And Control For Social Robots In Human Robot Interaction eBook

Author : Namrata Balakrishnan
Publisher :
Page : 100 pages
File Size : 14,51 MB
Release : 2019
Category :
ISBN :

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In the domain of social robotics, robots have recently been used in conversational interaction with humans. In this thesis, research was conducted to help create a system for imitation learning. In this system, a trainer trains a robot to be a teacher. The robotic teacher interacts with other humans in order to teach them the task that the robot was trained on. The method of 'Teaching by Demonstration' was used, where an ideal motion is performed by a trainer. This ideal motion is learned by the robot and replayed in the subsequent interaction with humans. If the replayed motion is copied by the human, the closeness of the motion performed by the human and the robot are compared. The task is then repeated until the desired optimal motion (as performed by the trainer) is obtained from the human subject. The main focus of the thesis is to define a general imitation system that can encode different motions which are beneficial in social robotics. A technique called Dynamic Movement Primitives (DMPs) was selected as the method for recording and generating the generalized robotic motions. The human-robot interaction gestures are compared using another algorithm called Dynamic Time Warping (DTW) and the validity of DTW as a comparison metric was also studied. DMPs are a set of non-linear differential equations which are used as the framework for describing human motion in a generalizable manner. A motion can be expressed as a combination of the learnt movement primitives. The DMPs have the flexibility to encode any motion into a set of differential equations by just adjusting certain parameters. The task/ motion that the robot is to teach a human subject is learnt using the DMPs. Once the motion is taught to the human subject, the gesture performed by the subject and the motion executed by the robot are juxtaposed and analyzed using the DTW method. DTW is an algorithm which analyzes motion series that change temporally. Similarities between the gestures performed by the robot and the imitation done by the human are studied using DTW. Trials are performed to validate the utility of DTW as an effective measure for comparison.

Author : Nicoletta Noceti
Publisher : Springer Nature
Page : 351 pages
File Size : 15,49 MB
Release : 2020-07-09
Category : Computers
ISBN : 3030467325

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The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.

Author : Chenguang Yang
Publisher : CRC Press
Page : 184 pages
File Size : 46,68 MB
Release : 2021-06-21
Category : Computers
ISBN : 1000395170

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In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.

Author : Erwin Prassler
Publisher : Springer Science & Business Media
Page : 434 pages
File Size : 29,12 MB
Release : 2004-10-27
Category : Technology & Engineering
ISBN : 9783540232117

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"Advances in Human-Robot Interaction" provides a unique collection of recent research in human-robot interaction. It covers the basic important research areas ranging from multi-modal interfaces, interpretation, interaction, learning, or motion coordination to topics such as physical interaction, systems, and architectures. The book addresses key issues of human-robot interaction concerned with perception, modelling, control, planning and cognition, covering a wide spectrum of applications. This includes interaction and communication with robots in manufacturing environments and the collaboration and co-existence with assistive robots in domestic environments. Among the presented examples are a robotic bartender, a new programming paradigm for a cleaning robot, or an approach to interactive teaching of a robot assistant in manufacturing environment. This carefully edited book reports on contributions from leading German academic institutions and industrial companies brought together within MORPHA, a 4 year project on interaction and communication between humans and anthropomorphic robot assistants.

Author : Chenguang Yang
Publisher : Elsevier
Page : 268 pages
File Size : 18,60 MB
Release : 2023-04-06
Category : Computers
ISBN : 0323958435

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Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning techniques. The book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting implementation tactics, adequate application examples and illustrative interpretations. Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect. Introduces research progress and technical contributions on teleoperation robots, including intelligent human-robot interactions and learning and control algorithms for teleoperation Presents control strategies and learning algorithms to a teleoperation framework to enhance human-robot shared control, bi-directional perception and intelligence of the teleoperation system Discusses several control and learning methods, describes the working implementation and shows how these methods can be applied to a specific and practical teleoperation system

Author : Aude Billard
Publisher : MIT Press
Page : 425 pages
File Size : 29,19 MB
Release : 2022-02-08
Category : 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.

Author : Zoe Doulgeri
Publisher : IET
Page : 265 pages
File Size : 26,81 MB
Release : 2023-09-26
Category : Technology & Engineering
ISBN : 1839535989

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This book covers important advances in the area of human-robot collaboration, aiming at future industrial applications. It will be useful to advanced students, researchers, engineers and entrepreneurs working on human-robot collaboration research and technologies, and related fields.

Author : Min Wu
Publisher : Logos Verlag Berlin GmbH
Page : 212 pages
File Size : 34,95 MB
Release : 2022-06-14
Category : Technology & Engineering
ISBN : 383255484X

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In recent years, researchers have achieved great success in guaranteeing safety in human-robot interaction, yielding a new generation of robots that can work with humans in close proximity, known as collaborative robots (cobots). However, due to the lack of ability to understand and coordinate with their human partners, the ``co'' in most cobots still refers to ``coexistence'' rather than ``collaboration''. This thesis aims to develop an adaptive learning and control framework with a novel physical and data-driven approach towards a real collaborative robot. The first part focuses on online human motion prediction. A comprehensive study on various motion prediction techniques is presented, including their scope of application, accuracy in different time scales, and implementation complexity. Based on this study, a hybrid approach that combines physically well-understood models with data-driven learning techniques is proposed and validated through a motion data set. The second part addresses interaction control in human-robot collaboration. An adaptive impedance control scheme with human reference estimation is presented. Reinforcement learning is used to find optimal control parameters to minimize a task-orient cost function without fully knowing the system dynamic. The proposed framework is experimentally validated through two benchmark applications for human-robot collaboration: object handover and cooperative object handling. Results show that the robot can provide reliable online human motion prediction, react early to human motion variation, make proactive contributions to physical collaborations, and behave compliantly in response to human forces.

Author : Wen Yu
Publisher : John Wiley & Sons
Page : 290 pages
File Size : 37,33 MB
Release : 2021-10-19
Category : Technology & Engineering
ISBN : 1119782740

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A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.

Author : Changliu Liu
Publisher : CRC Press
Page : 206 pages
File Size : 37,25 MB
Release : 2019-09-12
Category : Computers
ISBN : 0429602855

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In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc. Key Features: Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions. Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications. Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.