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2014 AAAI Fall Symposium

作者： Park, Hae Won Howard, Ayanna M. Human-Automation Systems Lab Georgia Institute of Technology 855th Street NW TSRB-444 AtlantaGA30092 United States

ISBN: (纸本)9781577356912

Programming a robot to perform tasks requires training that is beyond the skill level of most individuals. To address this issue, we focus on developing a method that identifies keywords used to convey task knowledge among people and a framework that uses these keywords as conditions for knowledge acquisition by the robot learner. The methodology includes generalizing task modeling and providing a robot learner the ability to learn and improve its skills through accumulated experience gained from interaction with humans. More specifically, the aim of this research addresses the issues of knowledge encoding. acquisition. and retrieval through interactive instance-based learning (IIBL). In interaction studies, the benefit of using such a robot learner is in promoting social behaviors that results from the participant taking on an active role as teacher. Our recent experiment with 33 participants, including 19 typically developing children, and a pilot study with two children with autism spectrum disorder showed that IlbL provides a framework for designing an effective robot learner, and that the robot learner successfully increases the amount of social interactions initiated by the participants. Copyright © 2014, Association for the Advancement of Artificial Intelligence.

关键词： Teaching

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Predicting the robot learning curve based on properties of human interaction

Predicting the robot learning curve based on properties of h...

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Agents that Learn from human Teachers - Papers from the AAAI Spring Symposium

作者： Remy, Sekou Howard, Ayanna M. Human-Automation System Lab Georgia Institute of Technology 225 North Avenue Atlanta GA 30332 United States

ISBN: (纸本)9781577354086

In this work we present research on three topics which have implications for future robotic applications. Couched in learning from human provided examples, we study how robots can demonstrate learning curves akin to those observed for human students. Specifically we show how the parameters of robot learning curves relate to those parameters from learning curves generated by human students. Next we show how these parameters and learning process they represent are affected by the quality of instruction provided. Finally, we present a method to generate an estimate of the robot learning curve. This method is of merit since it is based on properties of interaction that can be extracted as learning occurs. Copyright © 2009, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： human robot interaction

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Encouraging specific intervention motions via a robotic system for rehabilitation of hand function: A healthy pilot study

Encouraging specific intervention motions via a robotic syst...

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IEEE Symposium on Computational Intelligence in Robotic Rehabilitation and Assistive Technologies (CIR2AT)

作者： Brittney A English Ayanna M Howard Human-Automation Systems (HumAnS) Laboratory Georgia Institute of Technology Atlanta Georgia

A knowledge gap exists for how to improve hand rehabilitation after stroke using robotic rehabilitation methods, and non-robotic hand rehabilitation methods show only small patient improvements. A proposed solution for this knowledge gap is to integrate the strengths of three of the most favorable rehabilitation strategies for post-stroke rehabilitation of hand function, which are constraint-induced movement therapy (CIMT), high-intensity therapy, and repetitive task training, with a robotic rehabilitation gaming system. To create a system that is composed of collaborative therapy efforts, we must first understand how to encourage rehabilitation intervention motions. An experiment was conducted in which healthy participants were asked to complete six levels of a rehabilitation game, each level designed to encourage a specific therapeutic intervention, and a control, where participants were asked to complete undefined exercise motions. The results showed that participants' motions were significantly different than the control while playing each of the levels. Upon comparing the actual paths of participants to the paths encouraged by the levels, it was discovered that the participants followed the intended path while encouragement was being provided for them to do so. When the encouraged motions required quick, hard motions, the participants would follow an aliased version of the intended path. This study suggests that robotic rehabilitation systems can not only change how a participant moves, but also encourage specific motions designed to mimic therapeutic interventions.

关键词： Wrist Algorithm design and analysis Games Medical treatment Oscillators Legged locomotion

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The Effects of Adjusting Task Difficulty on Learning Motor and Cognitive Aspects of A Multitasking Task

The Effects of Adjusting Task Difficulty on Learning Motor a...

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IEEE Symposium Series on Computational Intelligence

作者： Brittney A. English Ayanna Howard Human-Automation Systems (HumAnS) Laboratory Georgia Institute of Technology Atlanta Georgia

Therapists have begun using musical techniques to teach motor skills in therapy sessions. To simulate these efforts, we have developed an app game that uses musical therapy techniques to teach simple tapping sequences to participants. Seven unique tapping sequences with varying levels of difficulty were taught to 208 participants. Two performance metrics were measured: (1) how quickly participants learned the correct number of taps in the sequence and (2) how quickly participants learned the correct frequency to tap the sequence. The first performance metric is a counting task, which is a cognitive function. The second performance metric is the speed at which the participant must move, which is a motor function. The results of our experiment show that when the difficulty of the tapping sequence was adjusted, participants learned the cognitive portion of the task more quickly when the task was simpler and the motor portion of the task more quickly when the task was more difficult. These trends were statistically significant with p-values < 0.05. Our experiment suggests that in context of teaching a simple tapping sequence, participants are multitasking cognitive and motor aspects of the task, as they multitask motor and cognitive skills while learning rehabilitation exercises in therapeutic settings. If these benefits could be transferred to a stroke survivor population in a therapeutic setting, an expedited recovery could be realized by stroke survivors, allowing them to regain motor functions, and therefore daily living activities, more quickly.

关键词： Task analysis Medical treatment Games Rhythm Robots Multitasking

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A probabilistic model for the performance analysis of a distributed task allocation algorithm

A probabilistic model for the performance analysis of a dist...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Antidio Viguria Ayanna M. Howard Human-Automation Systems Laboratory Georgia Institute of Technology Atlanta USA Human-Automation Systems Laboratory School of Electrical And Computer Engineering Georgia Institute of Technology Atlanta USA

In this paper we extend our previous work where the mean of the global cost was used as a performance metric for distributed task allocation algorithms. In this case, we move a step forward and calculate the variance of the global cost. This second parameter gives us a better understanding of the distributed algorithm performance, i.e., we can estimate how much the algorithm behavior diverts from its mean. The normal distribution, computed from the theoretical mean and variance, is shown to be suitable for modeling the global cost. This approximation enables us to compare our algorithm theoretically in different cases.

关键词： Performance analysis Robots Costs Distributed algorithms USA Councils Distributed computing Robotics and automation Aerospace engineering Measurement Gaussian distribution

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Providing tablets as collaborative-task workspace for human-robot interaction 13

Providing tablets as collaborative-task workspace for human-...

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ACM/IEEE International Conference on human-Robot Interaction (HRI)

作者： Hae Won Park Ayanna Howard Human-Automation Systems Laboratory Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467330558

In a recent conference on assistive technology in special education and rehabilitation, over 54 percentage of the sessions were directly or indirectly involved with tablets. Following this trend, many traditional assistive technologies are now transitioning from standalone devices into apps on mobile devices. As such, this paper follows this trend by discussing transforming a tablet into an HRI research platform where our robotic system engages the user in social interaction by learning how to operate a given app (task) using guidance from the user. The objective is to engage the robot within the context of the user's task by understanding the task's underlying rules and structures. An overview of the HRI toolkit is presented and a knowledge-based approach in modeling a task is discussed where previously learned cases are reused to solve a new problem.

关键词： Robot sensing systems Databases Education Context Cognition Knowledge based systems

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Applying Behavioral Strategies for Student Engagement Using a Robotic Educational Agent

Applying Behavioral Strategies for Student Engagement Using ...

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IEEE International Conference on systems, Man, and Cybernetics

作者： LaVonda Brown Ryan Kerwin Ayanna M. Howard Human-Automation Systems (HumAnS) Lab Georgia Institute of Technology Atlanta GA USA Mobile Robotics Lab (MRL) Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781479906505

Adaptive learning is an educational method that utilizes computers as an interactive teaching device. Intelligent tutoring systems, or educational agents, use adaptive learning techniques to adapt to each student's needs and learning styles in order to individualize learning. Effective educational agents should accomplish two essential goals during the learning process - 1) monitor engagement of the student during the interaction and 2) apply behavioral strategies to maintain the student's attention when engagement decreases. This paper focuses on the second objective of reengaging students using various behavioral strategies through the utilization of a robotic educational agent. Details are provided on the overall system approach and the forms of verbal and nonverbal cues used by the robotic agent. Results derived from 24 students engaging with the robot during a computer-based math test show that, while various forms of behavioral strategies increase test performance, combinations of verbal cues result in a slightly better outcome.

关键词： social robotics educational agents engagement engagement engaging Students Agents Learning Motivation Learning methods Computers

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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The Effects of Musical Cues on Motor Learning Using a Robotic Wrist Rehabilitation System - A Healthy Pilot Study

The Effects of Musical Cues on Motor Learning Using a Roboti...

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IEEE Workshop on Advanced Robotics and its Social Impacts

作者： Brittney A. English Ayanna M. Howard Human-Automation Systems (HumAnS) Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9781509004768

A robotic wrist rehabilitation system consisting of a passive therapy device and rehabilitation gaming suite was developed for upper limb motor rehabilitation. To enhance motor learning, musical cues were integrated into the gaming suite to encourage specific wrist movements. To validate the effects, a pilot study was conducted with abled-bodied participants to compare three conditions - the use of musical cues, the use of tapping beats and a condition with no sound. Participants were asked to complete a 90 second exercise session in which they used the therapy device to control the movements of a virtual pick to catch musical notes depicted on screen. Participants were randomly assigned to one of the three conditions. Participants who received musical cues reacted more quickly to musical notes, hit the positions more accurately, and showed a trend of missing fewer notes as compared to the control. These participants also continued the trend of reducing their miss rate after the musical cues were removed. This suggests that musical cues can be used to assist with motor task learning and retention.

关键词： Pilot Studies Motor Development Wrist Lake Restoration Rehabilitation miss rate Music Motor

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Transfer of skills between human operators through haptic training with robot coordination

Transfer of skills between human operators through haptic tr...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Chung Hyuk Park Jae Wook Yoo Ayanna M. Howard Human Automation Systems (HumAnS) Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology USA College of Computing Georgia Institute of Technology USA

ISBN: (纸本)9781424450381;9781424450404

In this paper, we discuss a coordinated haptic training architecture useful for transferring expertise in teleoperation-based manipulation between two human users. The objective is to construct a reality-based haptic interaction system for knowledge transfer by linking an expert's skill with robotic movement in real time. The benefits from this approach include 1) a representation of an expert's knowledge into a more compact and general form by learning from a minimized set of training samples, and 2) an increase in the capability of a novice user by coupling learned skills absorbed by a robotic system with haptic feedback. In order to evaluate our ideas and present the effectiveness of our paradigm, human handwriting is selected as our experiment of interest. For the learning algorithms, artificial neural network (ANN) and support vector machine (SVM) are utilized and their performances are compared. For the evaluation of the performance of the output of the learning modules, a modified Longest Common Subsequence (LCSS) algorithm is implemented. Results show that one or two experts' samples are sufficient for the generation of haptic training knowledge, which can successfully recreate manipulation motion with a robotic system and transfer haptic forces to an untrained user with a haptic device. Also in the case of handwriting comparison, the similarity measures result in up to an 88% match even with a minimized set of training samples.

关键词： humans Haptic interfaces Robot kinematics Artificial neural networks Support vector machines Knowledge transfer Joining processes Real time systems Neurofeedback Machine learning

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