BY ROBIN R. MURPHY,TATSUYA NOMURA,

AUDE BILLARD,AND JENNIFER L. BURKE

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An Exclusive Course forComputer Scientists and Engineers

The field of human–robot interaction (HRI)addresses the design, understanding, and evaluationof robotic systems, which involve humans androbots interacting through communication [1]. Asthe field matures, education of stu-

dents becomes increasingly important.Courses in HRI provide the canoni-cal set of knowledge and core skillsthat represent the current state of thefield and permit the evolution ofknowledge and methods to be trans-ferred from research to a broad set ofstudents. In addition, coursework in HRIcreates a workforce capable of transferring

HRI theory to practice. However, as would be expected with anemerging field, HRI courses are largely ad hoc.

Teaching HRI is challenging because the subject is multidis-ciplinary, and there is lack of educational materials, such as text-

books and resources such as robots and interfaces.This article summarizes the discussion and

findings from the “Teaching HumansAbout Human–Robot Interaction”workshop on the development of anHRI course for computer scientists andengineers. This half-day workshop was

held at the IEEE/Robotics Society ofJapan International Conference on Intelli-

gent Robots and Systems (IROS), 22 September2008, in Nice, France. The motivation for the workshop was adirect response to a key finding from the National ScienceDigital Object Identifier 10.1109/MRA.2010.936953

© CORBIS & JOHN FOXX

Foundation (NSF)-sponsored HRI Young Pioneers Workshops[2], held in conjunction with the annual Association of Comput-ing Machinery (ACM)/IEEE Conference on Human–RobotInteraction. The findings consistently emphasized the need foran interdisciplinary course or curriculum in HRI to be taught atthe university level. However, until the IROS workshop, therehas been no reported venue for faculty to gather and discuss sucha curriculum or teaching methods. Although this workshop waslimited in both time and the number of participants, it offers astarting point and some insight into HRI education.

The objectives of the workshop were to identify what isessential in an HRI course by leveraging the experiences todate in teaching HRI and then use this list of fundamentals todefine course content. The workshop was also expected tocreate a community of educators within the emerging HRIresearch community, foster the exchange of best practices andpedagogical methods, and provide reference materials, if any,for instructors teaching HRI.

The rest of this article is organized as follows. It firstdescribes the workshop in terms of participants and activities.The challenges for a course in HRI identified by the partici-pants follow next. Suggested course content, both in terms ofthe set of candidate topics for a course, and a sequence of lec-tures for advanced students in artificial intelligence (AI) androbotics follows. Next, possible course projects and assignmentsare discussed. The article then concludes with a distillation ofthe workshop into a set of six major findings.

Workshop DescriptionThe workshop was attended by 18 participants, representingFrance, Germany, India, Japan, Korea, Switzerland, and the

United States, and was organized around group discussions.Graduate students slightly exceeded the number of professorsand industry researchers. The workshop consisted of fourparts; beginning with each participant positing what theybelieved should be included in a HRI course and what is cur-rently missing from HRI education.

Second, a discussion on available resources for HRI educa-tion was initiated with an invited talk by Dr. Kojiro Matsushitafrom the University of Tokyo and demonstration of his twolow-cost robot kits [3]. One kit was made from servomotorsand plastic water bottles and can be constructed by beginnersin less than 6 h after 2 h of instruction, making it suitable fornonrobotics students. The resulting robot can take many con-figurations, including legs and snake structures, and moreemotive shapes similar to puppets. The robot can be controlleddirectly, learn motions from the user guiding the robot, orcontrolled by noninvasive contact sensors measuring musclestrain. Another kit is based on a toy hand. Dr. Matsushita isworking on an English translation of his book on how to buildand use these robots.

The next discussion, led by Dr. Aude Billard from theEcole Polytechnique Federale de Lausanne (EPFL), concen-trated on lessons learned from both instructor and studentexperiences with HRI. Three professors described HRI classesat the Indian Institute of Information Technology Allahabad(Prof. G.C. Nandi), EPFL (Prof. Aude Billard), and at theUniversity of South and Texas A&M (Prof. Robin Murphy).Rod Gutierrez, a graduate student at the University of SouthFlorida, presented feedback from the 2008 Young PioneersWorkshop at the 2008 ACM/IEEE International Conferenceon Human–Robot Interaction with amplifying commentsfrom that workshops attendees.

The fourth discussion took the form of breakout groups.Participants were split into two groups: one to discuss the per-fect syllabus, or sequence of lectures, for an HRI course, andthe other to determine the perfect set of assignments and proj-ects (Figure 1). The groups then gave reports summarizingtheir thoughts. The fifth component was a short recap anddiscussion of future activities, primarily increased involvementin the annual HRI conference.

ChallengesCreating a new course is always challenging, but the field ofHRI provides three additional challenges for education.

First, HRI is multidisciplinary, incorporating contributionsfrom communications, computer science, engineering, psy-chology, and theater, creating challenges in creating coursecontent that covers the field in sufficient depth without requir-ing a large number of prerequisites. Balancing coverage depthwhile minimizing prerequisites is particularly hard because thebackground between the engineering sciences and the humansciences was felt to be large. In response, the workshop partici-pants quickly restricted discussion to teaching students in theengineering sciences; even within that restriction, the differen-ces between individual engineering disciplines and computerscience were significant.

Figure 1. Participants in one of the two breakout groups.(Photo courtesy of Robin Murphy.)

The IEEE Robotics and AutomationSociety sponsors a TechnicalCommittee on Human–Robot Interaction.

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Second, the diversity of the HRI field also extends toresources, and as a result, there are no dedicated HRI resour-ces, although possible materials can be extracted from maturefields. For example, HRI does not have a journal or text-book. There is a dedicated conference, the annual ACM/IEEE International Conference on HRI now in its fourthyear, but the majority of participants were not aware of theconference. A related conference, the IEEE InternationalSymposium on Robot and Human Interactive Communica-tion (Ro-Man), also publishes HRI research. The IEEERobotics and Automation Society sponsors a technical com-mittee on HRI.

Third, there is a lack of cost-effective, pedagogicallyappropriate robots and rich interfaces. As detailed below,hands-on projects in HRI are highly desirable. Robots suchas Lego Mindstorms are inexpensive and do not requireextensive programming expertise but, as noted by the stu-dents, may not provide sufficient capability to support keyHRI topics. Humanoid robots vary in price but often havesignificant limitations for general HRI topics. For example,the design of the HOAP-3 robot prevents the camera in thehead from seeing the hands, curtailing physical interactionand learning manipulation tasks. Few robots in any pricerange support human–computer interfaces such as haptics,touch screens, or gestures. Speech recognition remains unre-liable, obviating the easy application of natural language to asurvey course. One promising robot resource that addressesthe third challenge is the robot kits presented by Dr. Mat-sushita and shown in Figure 2.

Course ContentStarting from the discussion andthroughout the workshop, topics forinclusion in course emerged. The objec-tives of an HRI course were proposed,and a subset of these topics was arrangedinto one possible sequence of lecturesaimed at advanced robotics or AI stu-dents. The individual topics were notrated as to relative importance becauseof time constraints.

The topics not only largely borrowedfrom robotics, AI, and psychology themesbut also included more unique HRI sub-jects and applications. Robot control andhumanoid robot design and control weretwo robotcentric topics suggested forinclusion, along with user interfaces. Skillacquisition, often associated with tradi-tional robot learning, has been experienc-ing a renaissance with the new emphasisoffered by HRI. In particular, it wasnoted that students often do not under-stand limits on the range of motion ordegrees of freedom in humanoid robotsand thus become confused when tryingto generate naturalistic motions. Natural

language processing and machine learning, staples of AI, werealso deemed important. Psychology and cognitive engineeringtopics were tools and methods to measure HRI, joint attentiontheory, teams, and user-centered design. The participants notedthat there was no concise list of qualitative and quantitative eval-uation methods or tools, nor was there a clear mapping of partic-ular techniques to desired outcomes, e.g., what techniquewould be best to measure X? Social behaviors, emotion or affec-tive expressions, interaction modalities, social learning, userexpectations, safety, the Uncanny Valley, and ethics emerged asunique HRI topics. The topic of social behaviors actually iscomposed of two topics: one is “what are social behaviors?” andthe other is “how can robots be programmed to generate socialbehaviors?” Rehabilitation and therapy was singled out as majorHRI application areas.

Course objectives should include at a minimum:u definition of HRIu the basic modalities for interacting with a robotu the key issues in HRIu the current applicationsu the process of making robots into social platformsu the importance of social skills in robots (role of learning,

a theory of mind).

Robot control and humanoid robotdesign and control were two

robotcentric topics suggested forinclusion.

(a) (b)

Figure 2. Demonstration of walking robot and robot hand kits by Dr. Matsushita. (a)Legged robot from water bottles and (b) robot hand. (Photo courtesy of RodrigoGutierrez.)

IEEE Robotics & Automation MagazineJUNE 2010 87

The prerequisites for an HRI course depend on the targetaudience and scope of material, although probability and sta-tistics was considered a universal prerequisite. In addition toprobability and statistics, related concepts such as regressionanalysis and experimental design would be helpful for acourse focused on methodology. Robotics and AI (captur-ing control and automation), sensors, and machine visionare starting points for robotics students to study HRI. Inaddition, having signal processing and machine learningmight be very helpful, although participants noted thatmachine learning was a topic that should also be covered inthe course.

Assuming an advanced robotics student with a backgroundin AI, a set of possible lectures spans robot inputs to ethics.These are:

u modalities and types of knowledge acquired throughinteractions, including vision, speech, and haptics

u representing the world and the intentions of othersu case studies of social learning and interactionu evaluation methodologies, both qualitative and quantitativeu ethics.

Course Projects and AssignmentsHaving a hands-on component to an HRI class was stronglyrecommended by those who have taught HRI, who wish toteach HRI, and students. The recommended pattern was tohave a series of small assignments either directly related tothe current course material or scaffolded in complexity, thena final project chosen by the students. Assignments and proj-ects directly involving robots and users were seen as the mostdesirable. However, working with users and robots raisesmany issues. Availability of platforms and of users is a con-cern. User-studies often requires a great deal of planning andpreparation, including getting any institutional human–sub-ject protocol approvals. Working with robots is costly, andthere are concerns that sufficient robots will not be opera-tional when needed. Robot simulations may prove to bea viable alternative to directly using a robot. Simulationssuch as Microsoft Robotics Studio can be programmed at ahigh level of abstraction, allowing the students to move anddirect the robot without having to focus on the details of therobot or robot programming. Regardless of whether real orsimulated robots are used, two applications are particularlyattractive for a course. Search and rescue robotics has astrong societal benefit, whereas social robots are engagingand entertaining.

Summary of FindingsThe workshop focused on teaching roboticists (computer sci-ence and engineers) at graduate level, generally discussingissues from an instructor’s viewpoint (e.g., pedagogy andresources) with a presentation and feedback from students.The six findings from the workshop are summarized below.

u Finding 1: Students prefer HRI courses with a highdegree of interaction between students and betweenstudents and robots over courses that are primarily lec-ture based. Interaction, both through discussion andhands-on projects, appears to be the desired style forteaching HRI.

u Finding 2: Candidate topics for coverage in an HRIcourse include emotion, ethics, humanoid robot designand control, interaction modalities, joint attention theory,machine learning, natural language processing, robotcontrol, safety, skill acquisition, social behaviors, sociallearning, teams, tools and methods to measure HRI, theUncanny Valley, user interfaces, user-centered design,and user expectations. The choice of topics to includedepends on the course prerequisites. On one hand, courseprerequisites permit content to go deeper or free up timein the course schedule to include more of these topics.On the other hand, prerequisites may exclude studentsfrom the human sciences or even from a particular engi-neering science discipline. This could undermine thebenefits of interdisciplinary courses and the discussion-oriented teaching style desired by the students.

u Finding 3: The most prominent deficits for creatingcourse content in HRI are the lack of: 1) a set of keyprinciples of HRI, 2) a survey of mechanisms on how togenerate social behaviors, and 3) a succinct synopsis ofuser evaluation methods. We note that the fist deficit inthe list reflects the lack of consensus in the HRIcommunity over HRI. However, the second and thirddeficits highlight gaps in robotics that must be filled bymultidisciplinary work; the second deficit shows theneed to connect control theory with the behavioral sci-ences, whereas the third deficit necessitates a transfer ofquantitative and qualitative methods pioneered outsideof robotics.

u Finding 4: The major missing pedagogical tools forinstructors are cost-effective robots and a corpus of casestudies, illustrating key principles of HRI. Cost isviewed as a major driver of a robot that can be adoptedby a large number of universities for teaching HRI.

u Finding 5: Course development should consider industryneeds as well as instructor constraints and student learn-ing preferences, as not all students will become HRIresearchers. This includes understanding anthropomor-phic robots as well as nonanthropomorphic forms.

u Finding 6: Regardless of the target audience, anHRI course will most likely require students to havea background in statistics and will, at a minimum,cover interaction modalities, issues, social interactions,and applications.

Psychology and cognitiveengineering topics were tools andmethods to measure HRI, jointattention theory, teams, and user-centered design.

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The workshop briefly touched on the way ahead. In termsof facilitating general progress in HRI education, there was ahope that the HRI conference would become a clearing housefor HRI-specific resources. In terms of continuing the discus-sion on HRI education, it would be interesting to elicit theviewpoints of other disciplines, especially psychology, onwhat they believe are fundamental topics and how HRIshould be taught.

AcknowledgmentsThe authors thank Dr. Matsushita for his demonstration oflow-cost robots, Rod Gutierrez for his presentation and gen-eral assistance during the workshop, Dr. Ephriam Glinert forhis support of the HRI Young Pioneers Workshop (NSFGrant IS-0813909), and the IROS 2008 tutorial chairs, Dr.Rachid Alami and Dr. Roland Siegwart.

KeywordsHuman–robot interaction, robotics education.

References[1] M. A. Goodrich and A. C. Schultz, “Human-robot interaction: A survey,”

Found. Trends Hum.-Comput. Interact., vol. 1, no. 3, pp. 203–275, 2007.[2] J. Burke, R. Murphy, and C. Kidd, “Young researchers in HRI workshop

2006,” Interact. Stud., vol. 8, no. 2, pp. 343–358, 2007.[3] K. Matsushita, H. Yokoi, and T. Arai, “Plastic-bottle-based robots in edu-

cational robotics courses—Understanding embodied artificial intelligence,”J. Robot. Mechatron., vol. 19, no. 2, pp. 212–222, 2007.

Robin R. Murphy received a B.M.E. degree in mechanicalengineering, and M.S. and Ph.D. degrees in computer science in1980, 1989, and 1992, respectively, from Georgia Tech, whereshe was a Rockwell International Doctoral Fellow. She is theRaytheon Professor of Computer Science and Engineering atTexas A&M. In 2008, she was awarded the Al Aube OutstandingContributor Award by the Association for Unmanned VehicleSystems International Foundation for her insertion of ground,air, and sea robots for urban search and rescue at the 9/11 WorldTrade Center disaster, Hurricanes Katrina and Charley, and theCrandall Canyon Utah mine collapse. She is a distinguishedspeaker for the IEEE Robotics and Automation Society and hasserved on numerous boards, including the Defense ScienceBoard, U.S. Air Force Scientific Advisory Board, NSF Computerand Information Science and Engineering Advisory Council,and the Defense Advanced Research Projects Agency Informa-tion Science and Technology Study Group. She is a SeniorMember of the IEEE. Her research interests include AI, HRI,and heterogeneous teams of robots.

Tatsuya Nomura received the M.S. degree in mathematicsfrom Osaka University, Japan, in 1989, and the D.E. degree inengineering from Kyoto University, Japan, in 1998. From 1989 to2000, he was with the Corporate Research and DevelopmentGroup at Sharp Corporation. From 2000 to 2004, he was withHannan University, Osaka, Japan. He is currently an associateprofessor in the Department of Media Informatics, RyukokuUniversity, Otsu, Japan, and a researcher in the Advanced

Technology and Research Intelligent Robotics and Commu-nication Laboratories, Japan. He is a member of the JapanesePsychological Association, the Japanese Cognitive ScienceSociety, and the Mathematical Society of Japan. He is a Mem-ber of the IEEE. His research interests include intelligentrobots and human—robot interaction.

Aude Billard received a B.Sc. degree in physics from EPFL,with specialization in particle physics at the European Center forNuclear Research (CERN) in 1994. She received her M.Sc.degrees in physics from the same university, with specialization inparticle physics at the CERN and in knowledge-based systems in1996 and a Ph.D. degree in AI from the Department of ArtificialIntelligence at the University of Edinburgh in 1998. She is anassociate professor and head of the Learning Algorithms and Sys-tems Laboratory at the School of Engineering, EPFL. Before this,she was a research assistant professor at the Department ofComputer Sciences at the University of Southern California,where she retained an adjunct faculty position to this day. She is aMember of the IEEE. Her research interests focus on machinelearning tools to support robot learning through human guid-ance. This extends also to research on complementary topics,including machine vision and its use in human–machine interac-tion and computational neuroscience to develop models oflearning in humans.

Jennifer L. Burke received the B.A. degree in business fromFlorida State University, the M.S. degree in counseling fromthe University of North Florida, and the M.S. and Ph.D.degrees in industrial-organizational psychology (minor: man–

machine interaction) from the University of South Florida, in1980, 1990, and 2006, respectively. She is a practicing humanfactors engineer at SA Technologies, specializing in roboticinterface design. She is active in the robotics and psychology/human factors communities and is the author of more than 30publications in fields of robotics, human performance, andworkplace studies. She is a member of the ACM, the Ameri-can Psychological Society, and the Human Factors and Ergo-nomics Society. Her research interests include team processesand human–robot interaction.

Address for Correspondence: Robin R. Murphy, ComputerScience and Engineering, Texas A&M University, CollegeStation, TX, USA. E-mail: murphy@cs.tamu.edu.

The prerequisites for an HRIcourse depend on the

target audience and scopeof material, although probability

and statistics was considered auniversal prerequisite.

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