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Author: Alyssa Kubota
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Cognitively assistive robots have great potential to improve the accessibility of healthcare services by extending existing clinical interventions to a person's home. This provides a variety of benefits, including extending the reach of professional services, allowing people to engage with these interventions at their own convenience, and reducing risk of exposure to illness at clinics. However, there are many obstacles to deploying these robots longitudinally and autonomously, particularly for populations with lower technology literacy such as older adults. These obstacles include enabling robots to leverage the expert domain knowledge of clinicians and other stakeholders, contextualizing the robot and intervention to the lives of users, and understanding and adapting to a person's intervention preferences and goals. The goal of my work is to design robots that can continuously learn from and adapt to people in real-world environments, which I explore in the context of delivering neurorehabilitation to people with cognitive impairments. In this dissertation, I will describe three main contributions of my work. First, I developed new methods to recognize complex motion reflective of real-world activities to enable robots to accurately understand human intention. Recognizing human activity can help robots understand a person's state and their reactions to its behavior. My work revealed the complementary strengths of two common sensor modalities for recognizing gross and fine motion, which can be leveraged to recognize complex activities and help robots better understand human intention. In addition, I designed a novel deep learning architecture for recognizing fine motion using nonvisual sensors, enabling robots to recognize human activity in dynamic, privacy sensitive settings such as homes. Second, I developed the first robotic system (JESSIE) which makes control synthesis accessible to novice programmers, allowing clinicians to quickly and easily specify complex robot behaviors through a tangible specification interface. Clinicians can provide robots with valuable domain and personal knowledge which can inform its behavior. My work revealed key insights regarding how robots can learn and adapt to people with cognitive impairments longitudinally at home. JESSIE makes control synthesis more accessible to novice programmers, enabling stakeholders to imbue robots with their domain knowledge and extend the reach of their work. Third, I developed an autonomous robot (CARMEN) which extends clinical interventions to the home, and longitudinally supports goal progress and motivation. In collaboration with clinicians and people with cognitive impairments, I identified interaction design patterns for translating clinical interventions to robots in order to maintain longitudinal engagement and maximize efficacy. Furthermore, I developed a new framework for roboticists creating longitudinal, robot-delivered health interventions with collaborative goal setting capabilities. My work lays the foundation for enabling robots to support motivation and goal achievement throughout a longitudinal intervention at home. My research contributes to building robotic systems which can longitudinally personalize their behavior to people in real-world environments. My work will transform how robots longitudinally interact with people, with the ultimate goal of enabling more safe and effective human-robot interaction, particularly for underserved populations.
Author: Alyssa Kubota
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Cognitively assistive robots have great potential to improve the accessibility of healthcare services by extending existing clinical interventions to a person's home. This provides a variety of benefits, including extending the reach of professional services, allowing people to engage with these interventions at their own convenience, and reducing risk of exposure to illness at clinics. However, there are many obstacles to deploying these robots longitudinally and autonomously, particularly for populations with lower technology literacy such as older adults. These obstacles include enabling robots to leverage the expert domain knowledge of clinicians and other stakeholders, contextualizing the robot and intervention to the lives of users, and understanding and adapting to a person's intervention preferences and goals. The goal of my work is to design robots that can continuously learn from and adapt to people in real-world environments, which I explore in the context of delivering neurorehabilitation to people with cognitive impairments. In this dissertation, I will describe three main contributions of my work. First, I developed new methods to recognize complex motion reflective of real-world activities to enable robots to accurately understand human intention. Recognizing human activity can help robots understand a person's state and their reactions to its behavior. My work revealed the complementary strengths of two common sensor modalities for recognizing gross and fine motion, which can be leveraged to recognize complex activities and help robots better understand human intention. In addition, I designed a novel deep learning architecture for recognizing fine motion using nonvisual sensors, enabling robots to recognize human activity in dynamic, privacy sensitive settings such as homes. Second, I developed the first robotic system (JESSIE) which makes control synthesis accessible to novice programmers, allowing clinicians to quickly and easily specify complex robot behaviors through a tangible specification interface. Clinicians can provide robots with valuable domain and personal knowledge which can inform its behavior. My work revealed key insights regarding how robots can learn and adapt to people with cognitive impairments longitudinally at home. JESSIE makes control synthesis more accessible to novice programmers, enabling stakeholders to imbue robots with their domain knowledge and extend the reach of their work. Third, I developed an autonomous robot (CARMEN) which extends clinical interventions to the home, and longitudinally supports goal progress and motivation. In collaboration with clinicians and people with cognitive impairments, I identified interaction design patterns for translating clinical interventions to robots in order to maintain longitudinal engagement and maximize efficacy. Furthermore, I developed a new framework for roboticists creating longitudinal, robot-delivered health interventions with collaborative goal setting capabilities. My work lays the foundation for enabling robots to support motivation and goal achievement throughout a longitudinal intervention at home. My research contributes to building robotic systems which can longitudinally personalize their behavior to people in real-world environments. My work will transform how robots longitudinally interact with people, with the ultimate goal of enabling more safe and effective human-robot interaction, particularly for underserved populations.
Author: Mengxi Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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The field of robotics has been rapidly evolving in recent years, and robots are being used in an ever-increasing number of applications, from manufacturing to healthcare to household chores. One of the key challenges in robotics is enabling robots to perform complex manipulation tasks in unstructured and dynamic environments. While there have been significant advances in robot learning and control, many existing approaches are limited by their reliance on pre-defined motion primitives or generic models that do not account for the specific characteristics of individual users, other cooperative agents or the interacting objects. In order to be effective in these various settings, robots need to be able to adapt to different tasks and environments, and to interact with different types of agents, such as humans and other robots. This thesis investigates learning approaches for enabling robots to adapt their behavior in order to achieve intelligent robot behavior. In the first part of this thesis, we focus on enabling robots to better adapt to humans. We start by exploring how to leverage different sources of data to achieve personalization for human users. Firstly, we investigate how humans prefer to teleoperate assistive robot arms using low-dimensional controllers, such as joysticks. We present an algorithm that can efficiently develop personalized control for assistive robots. Here the data is obtained by initially demonstrating the behavior of the robot and then query the user to collect their corresponding preferred teleoperation control input from the joysticks. Subsequently, we delve into the exploration of leveraging weaker signals to infer information from agents, such as physical corrections. Experiment results indicate that human corrections are correlated and reasoning over these corrections together achieves improved accuracy. Finally, instead of only adapting to a single human user, we investigate how robots can more efficiently cooperate with and influence human teams by reasoning and exploiting the team structure. We apply our framework to two types of group dynamics, leading-following and predator-prey, and demonstrate that robots can first develop a group representation and utilize this representation to successfully influence a group to achieve various goals. In the second part of this thesis, we extend our investigation from human users to robot agents. We tackle the problem of how decentralized robot teams can adapt to each other by observing only the actions of other agents. We identify the problem of an infinite reasoning loop within the team and propose a solution by assigning different roles, such as "speaker" and "listener, " to the robot agents. This approach enables us to treat observed actions as a communication channel, thereby achieving effective collaboration within the decentralized team. Moving on to the third part of this thesis, we explore the topic of adapting to different tasks by developing customized tools. We emphasize the critical role of tools in determining how a robot interacts with objects, making them important in customizing robots for specific tasks. To address this, we present an end-to-end framework to automatically learn tool morphology for contact-rich manipulation tasks by leveraging differentiable physics simulators. Finally, we conclude the thesis by summarizing our efforts and discussing future directions.
Author: Konstantinos Tsiakas
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 122
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Book Description
Robot-Assisted Training (RAT) is a growing body of research in Human-Robot Interaction (HRI) that studies how robots can assist humans during a physical or cognitive training task. Robot-Assisted Training systems have a wide range of applications,varying from physical and/or social assistance in post-stroke rehabilitation to intervention and therapy for children with Autism Spectrum Disorders. The main goal of such systems is to provide a personalized and tailored session that matches user abilities and needs, by adjusting task-related parameters (e.g., task difficulty, robot behavior), in order to enhance the effects of the training session. Moreover, such systems need to adapt their training strategy based on user's affective and cognitive states. Considering the sequential nature of human-robot interactions, Reinforcement Learning (RL) is an appropriate machine learning paradigm for solving sequential decision making problems with the potential to develop adaptive robots that adjust their behavior based on human abilities, preferences and needs. This research is motivated by the challenges that arise when different types of users are considered for real-time personalization using Reinforcement Learning, in a Robot-Assisted Training scenario. To this end, we present an Interactive Learning and Adaptation Framework for Personalized Robot-Assisted Training. This framework utilizes Interactive RL (IRL)methods to facilitate the adaptation of the robot to each individual, monitoring both behavioral (task performance) and physiological data (task engagement). We discuss how task engagement can be integrated to the personalization mechanism, through Learning from Feedback. Moreover, we show how Human-in-the-Loop approaches can be used to utilize human expertise using informative control interfaces, towards a safe and tailored interaction. We illustrate this framework with a Socially Assistive Robotic (SAR) system that instructs and monitors a cognitive training task and adjusts task diculty and robot behavior, in order to provide a personalized training session. We present our data-driven approach (data collection, data analysis, user modeling and simulation), as well as a user study to evaluate our real-time SAR-based prototype system for personalized cognitive training. We discuss the limitations and challenges of our approach, as well as possible future directions, considering the different modules of the proposed system (RL-based personalization, user modeling,EEG analysis, Human-in-the-Loop). The long-term goal of this research is to develop personalized and co-adaptive human-robot interactive systems, where both agents(human, robot) adapt and learn from each other, in order to establish an efficient interaction.
Author: Christina Moro
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Caregivers play a crucial role in assisting seniors having difficulty accomplishing activities of daily living (ADLs) due to physical or cognitive limitations. A global decline in the caregiver-to-senior ratio is making it increasingly more difficult to care for these seniors. Socially assistive robots are promising alternative technologies for supporting seniors in living independently. However, limited research has gone into developing a learning-based method for designing assistive robot behaviors. This thesis aims to: (1) identify the key features necessary for assistive robots supporting seniors with cognitive impairments in completing ADLs; and (2) develop a novel behavior-learning architecture to teach robots how to display assistive behaviors using expert demonstrations and personalize these learned behaviors to the seniorâ s cognition using reinforcement learning to increase task performance. Experiments with a socially assistive robot validated the robotâ s ability to learn and personalize new behaviors to a userâ s cognition from expert demonstration using the proposed architecture.
Author: Céline Jost
Publisher: Springer Nature
ISBN: 3030423077
Category : Social Science
Languages : en
Pages : 418
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Book Description
This book offers the first comprehensive yet critical overview of methods used to evaluate interaction between humans and social robots. It reviews commonly used evaluation methods, and shows that they are not always suitable for this purpose. Using representative case studies, the book identifies good and bad practices for evaluating human-robot interactions and proposes new standardized processes as well as recommendations, carefully developed on the basis of intensive discussions between specialists in various HRI-related disciplines, e.g. psychology, ethology, ergonomics, sociology, ethnography, robotics, and computer science. The book is the result of a close, long-standing collaboration between the editors and the invited contributors, including, but not limited to, their inspiring discussions at the workshop on Evaluation Methods Standardization for Human-Robot Interaction (EMSHRI), which have been organized yearly since 2015. By highlighting and weighing good and bad practices in evaluation design for HRI, the book will stimulate the scientific community to search for better solutions, take advantages of interdisciplinary collaborations, and encourage the development of new standards to accommodate the growing presence of robots in the day-to-day and social lives of human beings.
Author: Chang S. Nam
Publisher: Academic Press
ISBN: 0128194731
Category : Psychology
Languages : en
Pages : 614
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Book Description
Trust in Human-Robot Interaction addresses the gamut of factors that influence trust of robotic systems. The book presents the theory, fundamentals, techniques and diverse applications of the behavioral, cognitive and neural mechanisms of trust in human-robot interaction, covering topics like individual differences, transparency, communication, physical design, privacy and ethics. Presents a repository of the open questions and challenges in trust in HRI Includes contributions from many disciplines participating in HRI research, including psychology, neuroscience, sociology, engineering and computer science Examines human information processing as a foundation for understanding HRI Details the methods and techniques used to test and quantify trust in HRI
Author: Andrea Gaggioli
Publisher: IOS Press
ISBN: 1607500183
Category : Computers
Languages : en
Pages : 304
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Book Description
Intends to examine the focus and aims that drive rehabilitation intervention and technology development. This book addresses the questions of what research is taking place to develop rehabilitation, applied technology and how we have been able to modify and measure responses in both healthy and clinical populations using these technologies.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309388570
Category : Social Science
Languages : en
Pages : 525
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Book Description
Decades of research have demonstrated that the parent-child dyad and the environment of the familyâ€"which includes all primary caregiversâ€"are at the foundation of children's well- being and healthy development. From birth, children are learning and rely on parents and the other caregivers in their lives to protect and care for them. The impact of parents may never be greater than during the earliest years of life, when a child's brain is rapidly developing and when nearly all of her or his experiences are created and shaped by parents and the family environment. Parents help children build and refine their knowledge and skills, charting a trajectory for their health and well-being during childhood and beyond. The experience of parenting also impacts parents themselves. For instance, parenting can enrich and give focus to parents' lives; generate stress or calm; and create any number of emotions, including feelings of happiness, sadness, fulfillment, and anger. Parenting of young children today takes place in the context of significant ongoing developments. These include: a rapidly growing body of science on early childhood, increases in funding for programs and services for families, changing demographics of the U.S. population, and greater diversity of family structure. Additionally, parenting is increasingly being shaped by technology and increased access to information about parenting. Parenting Matters identifies parenting knowledge, attitudes, and practices associated with positive developmental outcomes in children ages 0-8; universal/preventive and targeted strategies used in a variety of settings that have been effective with parents of young children and that support the identified knowledge, attitudes, and practices; and barriers to and facilitators for parents' use of practices that lead to healthy child outcomes as well as their participation in effective programs and services. This report makes recommendations directed at an array of stakeholders, for promoting the wide-scale adoption of effective programs and services for parents and on areas that warrant further research to inform policy and practice. It is meant to serve as a roadmap for the future of parenting policy, research, and practice in the United States.
Author: Kerstin Dautenhahn
Publisher: Springer Science & Business Media
ISBN: 0306473739
Category : Computers
Languages : en
Pages : 297
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Book Description
Socially situated planning provides one mechanism for improving the social awareness ofagents. Obviously this work isin the preliminary stages and many of the limitation and the relationship to other work could not be addressed in such a short chapter. The chief limitation, of course, is the strong commitment to de?ning social reasoning solely atthe meta-level, which restricts the subtlety of social behavior. Nonetheless, our experience in some real-world military simulation applications suggest that the approach, even in its preliminary state, is adequate to model some social interactions, and certainly extends the sta- of-the art found in traditional training simulation systems. Acknowledgments This research was funded by the Army Research Institute under contract TAPC-ARI-BR References [1] J. Gratch. Emile: Marshalling passions in training and education. In Proceedings of the Fourth International Conference on Autonomous Agents, pages 325–332, New York, 2000. ACM Press. [2] J. Gratch and R. Hill. Continous planning and collaboration for command and control in joint synthetic battlespaces. In Proceedings of the 8th Conference on Computer Generated Forces and Behavioral Representation, Orlando, FL, 1999. [3] B. Grosz and S. Kraus. Collaborative plans for complex group action. Arti?cial Intelli gence, 86(2):269–357, 1996. [4] A. Ortony, G. L. Clore, and A. Collins. The Cognitive Structure of Emotions. Cambridge University Press, 1988. [5] R.W.PewandA.S.Mavor,editors. Modeling Human and Organizational Behavior. National Academy Press, Washington D.C., 1998.
Author: Michael Mahler
Publisher: Academic Press
ISBN: 032385432X
Category : Science
Languages : en
Pages : 300
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Book Description
Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions Provides background, milestone and examples of precision medicine Outlines the paradigm shift towards precision medicine driven by value-based systems Discusses future applications of precision medicine research using AI Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine
