# Introduction to Human-Robot Interaction:
A Multi-Perspective Introductory Course

Tom Williams  MIRRORLab
Department of Computer Science
Colorado School of MinesGolden, COUSA

###### Abstract.

In this paper I describe the design of an introductory course in Human-Robot
Interaction. This project-driven course is designed to introduce undergraduate
and graduate engineering students, especially those enrolled in Computer
Science, Mechanical Engineering, and Robotics degree programs, to key theories
and methods used in the field of Human-Robot Interaction that they would
otherwise be unlikely to see in those degree programs. To achieve this aim,
the course takes students all the way from stakeholder analysis to empirical
evaluation, covering and integrating key Qualitative, Design, Computational,
and Quantitative methods along the way. I detail the goals, audience, and
format of the course, and provide a detailed walkthrough of the course
syllabus.

Human-Robot Interaction; Engineering Education; Qualitative Methods; Design
Methods; Quantitative Methods

##  1\. Introduction and Course Goals

In this paper I describe the design of an introductory course in Human-Robot
Interaction. This project-driven course is designed to introduce undergraduate
and graduate engineering students, especially those enrolled in Computer
Science, Mechanical Engineering, and Robotics degree programs, to key theories
and methods used in the field of Human-Robot Interaction that they would
otherwise be unlikely to see in those degree programs. To achieve this aim,
the course takes students all the way from stakeholder analysis to empirical
evaluation, covering and integrating key Qualitative, Design, Computational,
and Quantitative methods along the way.

The key goal of this course is three-fold. First, the course aims to introduce
students to the notion of social robotics, and the idea of using interactive
robots to help meet the needs of real world human communities. Second, the
course aims to introduce students to the field of Human-Robot Interaction, and
to showcase both the types of work that researchers are doing in the field to
align with this vision of social robotics, as well as the research methods
that HRI researchers used to achieve those goals. Finally, the course aims to
operate as an HCI research methods course, where students can learn key tools,
including qualitative research methodologies, design research methodologies,
experimental design, and statistical analysis, which they could easily
transfer to other engineering projects, regardless of whether they choose to
pursue future work in HRI, or even in Computer Science at all.

##  2\. Prerequisites and Target Audience

These course goals are critically conditioned on the expected background of
the enrolled students. The course is offered at a small engineering-only
university with a strong focus on Robotics related fields (50% of all
undergraduate students are enrolled in Mechanical Engineering or Computer
Science degree programs, and degree programs offered in Robotics at both the
undergraduate and graduate level), but with no degree programs offered in
social sciences or humanities (e.g., Psychology) and few, if any, elective
courses available in those fields. The university size and focus means that
the course is offered at a mixed undergraduate/graduate level, and is
primarily offered to students from Computer Science, Mechanical Engineering,
and Electrical Engineering. Based on this university structure and student
makeup, students enrolled in the course have prerequisite programming and
mathematical knowledge, have likely taken related technical courses such as
Intro to Robotics, Robot Perception, SLAM, Robot Planning, Computer Vision,
Robot Ethics, Mechatronics, Advanced Robotic Control, or Robot Mechanics, and
have likely taken key design courses required of all undergraduate students,
but likely have little exposure to or appreciation for relevant theories,
methods, or practices from psychology, philosophy, communication, and so
forth. These assets and deficits critically shape the content covered in the
course.

One surprising way these assets and deficits shape course design is in terms
of the course’s coverage of robot ethics topics (or lack thereof). Most
students enrolled in the course also take Robot Ethics either before or after
taking this course. This is especially true for graduate students enrolled in
a Robotics MS or PhD program, who are required to take one or both courses. As
such, despite the societal and ethical impacts of interactive robots being
absolutely critical to the course content, these topics are not explicitly
covered since it is assumed that most students will receive deep coverage of
those topics in the standalone Robot Ethics course. That being said, key
ethical frameworks such as Engineering for Social Justice (E4SJ)e (Leydens and
Lucena, 2017), Robots for Social Justice (Zhu et al., 2024), and Feminist
Human-Robot Interaction (Winkle et al., 2023) are baked into the course and
reflected in course activities such as stakeholder analysis, needfinding, and
E4SJ-grounded reflection exercises. Moreover, discussion of social and ethical
implications frequently arise in guest lectures invited in the second half of
the semester.

##  3\. Course Format Overview

To achieve the course goals, the course is structured as a 48-student,
project-based, sixteen-week course with an even balance between lectures and
lab assignments. Several lab assignments and lectures are derived from
reference courses, especially Dr. Ana Paiva’s Masters-level Social Robots and
Human Robot Interaction course, and Bilge Mutlu’s graduate-level Human-
Computer Interaction course, albeit adapted for this course’s mixed
undergraduate/graduate population.

All 48 students attend class Mondays and Wednesdays for 50 minutes each day.
Students also attend one of two 24-student, two-hour long, lab section on
Fridays. Course exercises use nine Softbank Naos purchased for the course over
several years through university-internal course equipment grants. At any
given time, course staff ensure that eight of the nine Naos are charged and
operational. As such, each lab section can be broken into eight three-student
project groups, each of which has a Nao made available to them during lab
sections.

Over the course of the semester, students submit all lab reports and other
project deliverables by uploading them to Open Science Framework (OSF)
repositories, thus teaching the students open science principles and tools as
a secondary learning outcome.

##  4\. Detailed Syllabus

In this section I will go through the course syllabus week by week, to explain
how the different elements of the course fit together.

The first week begins with a set of activities intended to convey a high level
sense of the course to students. Specifically, the first class immediately
establishes the importance of grounding robotic engineering practice in
genuine community needs, and the ability of qualitative methods to establish
this grounding. Students are then immediately started on their first major
assignment, in which they are tasked with interviewing someone of their choice
who works in an industry relevant to social robotics, about the needs they
face. In this first week, a panel of HRI industry professionals also attend
class, to help students see this first assignment, which is likely out of
their comfort zone, as an opportunity to learn a skill relevant to their
future robotics careers. Finally, in this first week, students participate in
their first Nao programming lab, to better understand the capabilities of the
robot they will use in the class, and better see connections with the needs of
the stakeholders they are interviewing.

The second week of the course steps back to explore the theoretical
foundations of HRI, including key dimensions of interactions and
interactivity, and of longer-term constructs such as trust and influence.
Finally, students end the week by performing a multi-day grounded theory
analysis of their interview transcripts, modeled after an assignment from
Bilge Mutlu’s HCI course at University of Wisconsin Madison. In this
assignment, students begin by performing open coding together in class.
Students then separately perform axial coating as homework, and then reconvene
in groups to consolidate their axial codes and identify higher level trends in
their collected interview transcripts. During this week, students are also
tasked with performing brief literature reviews to supplement what they are
hearing in their interviews with what others have heard and determined in
other parts of the field.

In the third week of the class, class is devoted to collaborative exercises in
which students work towards a shared understanding of the goals they will
pursue over the course of the semester. Students begin by taking insights from
their interviews, and insights from their literature reviews, and creating
sets of need-reason-source triads that correspond with key user needs,
associated reasons why those needs should be prioritized in robot design (if
possible. And the source of that reason in their interview or literature
review. This helps to ensure that student projects are grounded in real user
needs and are traceable back to those needs. Next, students use these need-
reason-source triads to create vision statements for their class projects.
Third, students engage in structured reflection exercises in which students
are encouraged to reflect on ethical design principles from Engineering for
Social Justice (Leydens and Lucena, 2017), and are given the opportunity to
revise their vision statements accordingly. Finally, students identify key
robot design goals aligning with their final vision statements, and perform a
card sorting exercise to identify which design goals they will actually
prioritize over the course of the semester.

In the fourth week of the class, students are given the tools they need to
pursue their design goals. We begin with a lecture on robot design, followed
by a robot design lab in which students first storyboard out interactions
(e.g., Fig. 1), then learn principles of improvisational theater through
Improv theater games played outside on the campus quad, then apply those
principles to perform roleplay–based embodied sketching exercises. These
exercises help students identify possible interaction designs that will help
them best achieve their design goals. Students then perform further literature
review to identify other interaction design strategies from the literature
that might also help them achieve their design goals. Finally, students end
this fourth week with midterm presentations in which they present the results
of their interviews, their design visions, design goals, and the design
strategies they plan to use to pursue those goals and visions to meet the
needs of their identified user populations.

Figure 1. Storyboard from a project group in Fall 2022

In weeks five through eight, students learn the technical skills they need to
execute their design strategies. In week five, we focus on spatial and non-
verbal interaction. Students receive lectures on proxemics, motion, and gaze,
and begin a lab assignment on robot perception, grounded in the official Nao
tutorials. In week six, we focus on interaction dynamics. Students receive
lectures on group and team structures, collaboration, and turn-taking, and
complete their perception lab assignment. In week seven, we focus on verbal
interaction. Students receive lectures on dialogue, reference, and grounding,
and begin a lab assignment on robot dialogue, grounded in the official Nao
tutorials. Finally, in week eight, we focus on emotion and personality.
Students receive lectures on emotion and character design, and finish their
dialogue lab assignment.

In weeks nine through twelve, students are given the tools needed to run
experiments to evaluate their designs. In week nine, students are taught about
key dimensions of quantitative research methods, including measures, metrics,
and experimental design. Students then begin a month-long experimental design
lab, in which they formulate research questions grounded in their design
strategies, formulate hypothesis that correspond with those research
questions, and design a video-based HRI experiment to test those hypotheses.
Students then use the design skills learned earlier in the semester to design
the interactions that will appear in their experimental stimuli, and use the
technical skills learned earlier in the semester to implement those designs on
the Nao robot. Students go through CITI training, and create research ethics
protocols and consent forms for their experiments. Finally, students implement
their video-based experiments through Google Forms, and run their experiments,
with each student in the class serving as a participant in each study designed
by a project group other than their own. During weeks ten through twelve,
students work on this lab in class together on Fridays and outside of classes
homework, while on Mondays and Wednesdays, guest speakers from other
universities provide guest lectures on a wide array of HRI topics. These guest
lectures broaden the scope of HRI topics to which students are exposed,
without adding additional assignments to the students workload.

In weeks thirteen and fourteen, students are given the statistical tools
needed to evaluate the data from the experiments they have run. In week
thirteen students learn about both Frequentist and Bayesian statistics, and
complete a two-week lab assignment, in which they first proceed through
external tutorials on performing data analyses in JASP, and in which they are
then invited to use the same analysis paradigms to analyze the results of
their own experiments. In week fourteen, students listen to another guest
lecture from an HRI researcher from another university while completing their
statistics lab assignment, and then are released on Thanksgiving break.

Finally, we close the semester in week fifteen and sixteen. In these last two
weeks, students hear a final invited talk from an external researcher, and
perform a design futures exercise in which they speculate about other possible
future uses for social robots beyond those they were able to explore in the
class. Students attend a course wrap up lecture in which they learn about
other classes they can take to supplement their knowledge, and learn about
where other HRI research is being performed across the US, and especially in
Colorado, in case they are interested in pursuing graduate work in HRI.
Finally, students give group presentations covering the entire life cycle of
their projects, from needfinding, to design, to implementation, to evaluation
and results.

##  5\. Assignments and Assessment

Over the course of the semester, students are assessed through several means.
All students in the course read key course readings (typically textbook
chapters from Bartneck et al. (2020)’s Human-Robot Interaction: An
Introduction) and are assessed using brief, simple, low-stakes quizzes on the
reading. In addition, graduate students in the class are asked to read a
series of HRI research papers relating to class topics, and are assessed using
forum-style discussion-board posts in which they engage with the content of
those papers.

Next, students are graded through project deliverables, including lab reports,
midterm and final presentations, a final video demonstration of the autonomous
interaction they designed for the Nao, and a final six-page HRI-style paper
reflecting the type of paper they could have submitted to HRI if they had
actually obtained IRB approval and run the experiment with naive participants
rather than pilot-testing the experiment on themselves. In previous years, I
have had students actually obtain IRB approval, and paid for student groups to
run twenty within-subject participants each on Prolific. However, in practice
the overhead needed for students to learn to use psiTurk, and the overhead
needed for the course staff to deploy and run these experiments on students’
behalf, was deemed prohibitively burdensome and not sufficiently contributing
to student learning.

Finally graduate students are asked to complete a four-page mini-survey on a
topic of their choice within HRI, in which they collect, compare, and contrast
at least 20-30 research papers on their chosen topic. At least half of these
papers are required to have been published at T-HRI, HRI, RO-MAN, or ICSR.

##  6\. Case Study

Each year, students in this course have explored a wide range of topics
through their semester-length projects. To provide a sense of these projects,
I will discuss the outcomes of one undergraduate project team as a case study.
Approval to provide this information was approved by our Human Subjects
Research board, under informed consent from students.

This project team began by interviewing an elementary school teacher about her
experiences in the classroom. Based on this interview, and inspired by the HRI
2018 paper “Stop. I See a Conflict Happening” (Shen et al., 2018) the students
formulated the following design vision:

> “A key goal in elementary classrooms identified was that students and
> teachers need to have individual interactions to assist with communication
> and manage conflicts. From the interview and axial codes, several issues
> that make it hard for teachers to achieve this goal were identified. These
> issues include that kids struggle with social interactions, get frustrated
> quickly, prefer iPads to social interaction, and teachers are often required
> to get involved in physical conflicts between students. In addition, meeting
> the needs of every student individually requires teachers and students to
> have one on one time. However, as seen in axial code, teachers are required
> to perform many varied and demanding classroom tasks and thus do not have
> the time or manpower to deal with and manage social interaction and conflict
> with students on an individual basis. We believe that social robots may be
> able to assist teachers in managing conflicts between students and help to
> teach positive social interaction. We believe that social robots address
> this problem because robots can interact one on one with students in
> scenarios where the teacher is busy, and can intervene in minor conflicts
> between students freeing the teacher to focus on other tasks. In addition,
> kids may be more willing to listen to advice and rules from a robot and be
> less likely to fight a robot.”

After formulating this power, students designed a conflict resolution
interaction using storyboarding and embodied sketching, implemented it on the
Nao robot using its Python API, and chose to study how different linguistic
choices might shape the persuasive power of the robot as derived from its
perceived authority. The students designed a 2 (Humorous/Serious) x 2
(Assertive/Passive) within-subjects experiment with a Latin Square design, and
measured the effects of these conditions on perceived authority using the
scale proposed by Gudjonsson (1989). Finally, the students analyzed their
results under a Bayesian analysis framework, and calculated Bayes Inclusion
factors for each of their considered factors and their interaction.

Overall, these activities demonstrated students’ (undergraduate-level) mastery
over qualitative, design, computational, and quantitative research
methodologies.

##  7\. Conclusion

Overall, Introduction to Human-Robot Interaction serves to introduce students
to the domain of Social Robotics, the field of Human-Robot Interaction, and
the research methodologies of Human-Computer Interaction. Due to the cross-
cutting nature of the course, the course does not delve deeply into (1) the
fundamental theories of HRI, (2) the algorithmic methods of HRI, or (3) the
social and ethical implications of HRI. Students taking this course
(especially graduate students) would thus be best served by taking relevant
courses in those areas before, concurrently with, or following the course.

###### Acknowledgements.

This work was funded in part by NSF CAREER Award IIS-2044865.

## References

  * (1)
  * Bartneck et al. (2020) Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Šabanović. 2020.  _Human-robot interaction: An introduction_.  Cambridge University Press.
  * Gudjonsson (1989) Gisli H Gudjonsson. 1989.  Compliance in an interrogative situation: A new scale.  _Personality and Individual differences_ 10, 5 (1989), 535–540.
  * Leydens and Lucena (2017) Jon A Leydens and Juan C Lucena. 2017.  _Chapter 2: Engineering Design for Social Justice_.  John Wiley & Sons.
  * Shen et al. (2018) Solace Shen, Petr Slovak, and Malte F Jung. 2018.  “Stop. I See a Conflict Happening.””” A Robot Mediator for Young Children’s Interpersonal Conflict Resolution. In _Proceedings of the 2018 ACM/IEEE international conference on human-robot interaction_. 69–77.
  * Winkle et al. (2023) Katie Winkle, Donald McMillan, Maria Arnelid, Katherine Harrison, Madeline Balaam, Ericka Johnson, and Iolanda Leite. 2023.  Feminist Human-Robot Interaction: Disentangling Power, Principles and Practice for Better, More Ethical HRI. In _Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction_ (Stockholm, Sweden) _(HRI ’23)_. Association for Computing Machinery, 72–82.
  * Zhu et al. (2024) Yifei Zhu, Ruchen Wen, and Tom Williams. 2024.  Robots for Social Justice (R4SJ): Toward a More Equitable Practice of Human-Robot Interaction. In _Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI)_.