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Human–Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology compa- nies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coher- ent “story” of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross appli- cations. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.
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Foundations and Trends R
Human–Computer Interaction
Vol. 1, No. 3 (2007) 203–275
2007 M. A. Goodrich and A. C. Schultz
DOI: 10.1561/1100000005
Human–Robot Interaction: A Survey
Michael A. Goodrich1and Alan C. Schultz2
1Brigham Young University, Provo, UT 84602, USA,
2US Naval Research Laboratory, Washington, DC 20375, USA,
Human–Robot Interaction (HRI) has recently received considerable
attention in the academic community, in labs, in technology compa-
nies, and through the media. Because of this attention, it is desirable
to present a survey of HRI to serve as a tutorial to people outside
the field and to promote discussion of a unified vision of HRI within
the field. The goal of this review is to present a unified treatment of
HRI-related problems, to identify key themes, and discuss challenge
problems that are likely to shape the field in the near future. Although
the review follows a survey structure, the goal of presenting a coher-
ent “story” of HRI means that there are necessarily some well-written,
intriguing, and influential papers that are not referenced. Instead of
trying to survey every paper, we describe the HRI story from multiple
perspectives with an eye toward identifying themes that cross appli-
cations. The survey attempts to include papers that represent a fair
cross section of the universities, government efforts, industry labs, and
countries that contribute to HRI, and a cross section of the disciplines
that contribute to the field, such as human, factors, robotics, cognitive
psychology, and design.
Human–Robot Interaction (HRI) is a field of study dedicated to under-
standing, designing, and evaluating robotic systems for use by or with
humans. Interaction, by definition, requires communication between
robots and humans. Communication between a human and a robot may
take several forms, but these forms are largely influenced by whether
the human and the robot are in close proximity to each other or not.
Thus, communication and, therefore, interaction can be separated into
two general categories:
Remote interaction — The human and the robot are not co-
located and are separated spatially or even temporally (for
example, the Mars Rovers are separated from earth both in
space and time).
Proximate interaction — The humans and the robots are co-
located (for example, service robots may be in the same room
as humans).
Within these general categories, it is useful to distinguish between
applications that require mobility, physical manipulation, or social
interaction. Remote interaction with mobile robots is often referred
to as teleoperation or supervisory control, and remote interaction with
a physical manipulator is often referred to as telemanipulation. Prox-
imate interaction with mobile robots may take the form of a robot
assistant, and proximate interaction may include a physical interac-
tion. Social interaction includes social, emotive, and cognitive aspects
of interaction. In social interaction, the humans and robots interact
as peers or companions. Importantly, social interactions with robots
appear to be proximate rather than remote. Because the volume of
work in social interactions is vast, we present only a brief survey; a more
complete survey of this important area is left to future work.
In this review, we present a survey of modern HRI. We begin by
presenting key developments in HRI-related fields with the goal of iden-
tifying critical technological and scientific developments that have made
it possible for HRI to develop as a field of its own. We argue that HRI is
not simply a reframing and reformulation of previous work, but rather
a new field of scientific study. To support this argument, we identify
seminal events that signal the emergence of HRI as a field. Although
we adopt a designer-centered framing of the review, work in the field
requires strong interdisciplinary blends from various scientific and engi-
neering fields.
After surveying key aspects in the emergence of HRI as a field, we
define the HRI problem with an emphasis on those factors of inter-
action that a designer can shape. We then proceed to describe the
application areas that drive much of modern HRI. Many of these prob-
lems are extremely challenging and have strong societal implications.
We group application areas into the previously mentioned two general
categories, remote and proximate interactions, and identify important,
influential, or thought-provoking work within these two categories. We
follow this by describing common solution concepts and barrier prob-
lems that cross application domains and interaction types. We then
briefly identify related work from other fields involving humans and
machines interacting, and summarize the review.
Early History of Robotics and
In this section, we briefly survey events and work that have made mod-
ern HRI possible. Clearly, the development of robots was the essen-
tial first step. Although robot technology was primarily developed
in the mid and late 20th century, it is important to note that the
notion of robot-like behavior and its implications for humans have
been around for centuries in religion, mythology, philosophy, and fic-
tion. The word “robot” originates from the Czechoslovakian word rob-
ota which means work [309]. “Robot” appears to have first been used
in Karel Chapek’s 1920’s play Rossum’s Universal Robots, though this
was by no means the earliest example of a human-like machine. Indeed,
Leonardo da Vinci sketched a mechanical man around 1495, which
has been evaluated for feasibility in modern times [250]. Pre-dating da
Vinci’s humanoid robot are automata and mechanical creatures from
ancient Egypt, Greece, and China. The Iliad refers to golden maids
that behave like real people [125]. The idea of golem, an “artificial
being of Hebrew folklore endowed with life” has been around for cen-
turies [309] and was discussed by Wiener in one of his books [315].
Ancient Chinese legends and compilations mention robot-like creations,
such as the story from the West Zhou Dynasty (1066BC–771BC) that
describes how the craftsman Yanshi presented a humanoid. The cre-
ation looked and moved so much like a human that, when it winked at
the concubines, it was necessary to dismantle it to prove that it was
an artificial creation [328]. Similar robotic devices, such as a wooden
ox and floating horse, were believed to have been invented by the Chi-
nese strategist Zhuge Liang [316], and a famous Chinese carpenter was
reported to have created a wooden/bamboo magpie that could stay
aloft for up to three days [297]. More recently, robotic-like automata,
including Vaucanson’s duck, have been created [243]. Mechanical-like
birds were present in the 1933 poem Byzantium by W. B. Yeats [326],
and robots have had a large presence in science fiction literature, most
notably Azimov’s works [12]. Indeed, Asimov’s Laws of Robotics appear
to be the first designer guidelines for HRI.
Early robot implementations were remotely operated devices with
no or minimal autonomy (Figure 2.1). In 1898, Nicola Tesla demon-
strated a radio-controlled boat, which he described as incorporating
“a borrowed mind.” In fact, Tesla controlled the boat remotely. His
invention, which he generalized to many different types of vehicles, was
described in patent 613,809, “Method and Apparatus for Controlling
Mechanism of Moving Vessels.” Tesla hypothesized, “ see there
Fig. 2.1 Tesla’s boat [287].
208 Early History of Robotics and Human–Machine-Interaction
the first of a race of robots, mechanical men which will do the laborious
work of the human race.” He even envisioned one or more operators
simultaneously directing 50 or 100 vehicles.
Other examples include: The Naval Research Laboratory’s “Elec-
tric Dog” robot from 1923, attempts to remotely pilot bombers during
World War II, the creation of remotely piloted vehicles, and mechan-
ical creatures designed to give the appearance of life. As technology
evolved, the capabilities of remotely operated robots have grown (see
[95] for a brief history). This is perhaps nowhere more evident then in
the very successful application of unmanned underwater vehicles that
have been used to explore the ocean’s surface to find lost ships, explore
underwater life, assist in underwater construction, and study geother-
mal activity [313].
Complementing the advances in robot mechanics, research in arti-
ficial intelligence has attempted to develop fully autonomous robots.
The most commonly cited example of an early autonomous robot was
Shakey, which was capable of navigating through a block world under
carefully controlled lighting conditions at the glacially slow speed of
approximately 2 meters per hour [209]. Many agree that these early
works laid a foundation for much that goes on in hybrid control archi-
tectures today [196, 223].
A breakthrough in autonomous robot technology occurred in the
mid 1980s with work in behavior-based robotics [10, 38]. Indeed, it
could be argued that this work is a foundation for many current robotic
applications. Behavior-based robotics breaks with the monolithic sense-
plan-act loop of a centralized system, and instead uses distributed
sense-response loops to generate appropriate responses to external stim-
uli. The combination of these distributed responses produces “emer-
gent” behavior that can produce very sophisticated responses that
are robust to changes in the environment. A second important break-
through for autonomy as it applies to HRI is the emergence of hybrid
architectures; these architectures simultaneously allow sophisticated
reactive behaviors that provide fundamental robot capabilities along
with the high-level cognitive reasoning required for complex and endur-
ing interactions with humans. Robot behaviors initially focused on
mobility, but more recent contributions seek to develop lifelike anthro-
pomorphic behaviors [323], acceptable behaviors of household robots
[158], and desirable behaviors for robots that follow, pass, or approach
humans [105, 220, 307].
The development of robust robot platforms and communications
technologies for extreme environments has been accomplished by NASA
and other international space agencies. Space agencies have had several
high profile robotic projects, designed with an eye toward safely explor-
ing remote planets and moons. Examples include early successes of the
Soviet Lunokhods [95] and NASA’s more recent success of exploring
the surface of Mars [174, 317]. Importantly, many of the failures have
been the result of software problems rather than mechanical failures.
Complementing NASA’s fielded robots have been several robots devel-
oped and evaluated on earth [17]. Robonaut is a well-known example
of successful teleoperation of a humanoid robot [9], and this work is
being extended at a rapid pace to include autonomous movement and
reasoning. Autonomous robots that have the anthropomorphic dimen-
sions, mimic human-like behaviors, and include human-like reasoning
are known as humanoid robots; work in this area has been ongoing for
over a decade and is rapidly expanding [9, 23, 31, 37, 153, 273, 285].
Emerging from the early work in robotics, human factors experts
have given considerable attention to two paradigms for human–robot
interaction: teleoperation and supervisory control. At the teleopera-
tion extreme, a human remotely controls a mobile robot or robotic
arm. With supervisory control, a human supervises the behavior of
an autonomous system and intervenes as necessary. Early work was
usually performed by people who were interested not only in robotics
but also factory automation, aviation, and intelligent vehicles. Work in
these areas is typified by Sheridan’s seminal contributions [267, 268],
and other significant contributions from human factors researchers
[193, 314].
Every robot application appears to have some form of interaction,
even those that might be considered “fully autonomous.” For a teleop-
erated robot, the type of interaction is obvious. For a fully autonomous
robot, the interaction may consist of high-level supervision and direc-
tion of the robot, with the human providing goals and with the robot
maintaining knowledge about the world, the task and its constraints.
210 Early History of Robotics and Human–Machine-Interaction
In addition, the interactions may be through observation of the environ-
ment and implicit communications by, for example, the robot respond-
ing to what its human peer is doing. Taking a very broad and general
view of HRI, one might consider that it includes developing algorithms,
programming, testing, refining, fielding, and maintaining the robots.
In this case, interaction consists primarily in discovering and diag-
nosing problems, solving these problems, and then reprogramming (or
rewiring) the robot. The difference between this type of “programming-
based” interaction and modern HRI is that the field currently empha-
sizes efficient and dynamic interactions rather than just infrequent
interactions. However, some researchers are addressing programming-
based of interaction by exploring efficient programming paradigms to
support robot development [128, 327].
Emergence of HRI as a Field
Although there is much work that can be considered HRI, the multi-
disciplinary field started to emerge in the mid 1990s and early years of
2000. Key numerous events occurred in this time frame, with the main
catalyst being a multi-disciplinary approach; researchers from robotics,
cognitive science, human factors, natural language, psychology, and
human–computer interaction started to come together at these events
specifically recognizing the importance of working together.
The earliest scientific meeting, which started in 1992 and continues
annually, is the IEEE International Symposium on Robot & Human
Interactive Communication (RoMan). Although recently this confer-
ence has attracted a more multi-disciplinary research community, his-
torically it has been heavily dominated by the robotics discipline. In
2000, the IEEE/Robotics Society of Japan created the International
Conference on Humanoid Robots which highlights anthropomorphic
robots and robotic behaviors.
From the late 1990s until recently, there have been many work-
shops and conference tracks dedicated to HRI, including ones associated
with the Association for the Advancement of Artificial Intelli-
gence’s (AAAI) Symposia Series, IEEE International Conference on
212 Emergence of HRI as a Field
Robotics and Automation (ICRA), Robotics Systems and Sciences, the
IEEE/Robotics Society of Japan International Conference on Intelli-
gent Robot and Systems, among others, and the annual meeting of the
Human Factors and Ergonomics Society.
In 2001, the US National Science Foundation and Defense Advanced
Research Projects Agency sponsored a workshop on human–robot inter-
action, organized by Dr Robin Murphy and Dr Erica Rogers [46]. The
purpose of this workshop was to bring together a highly multidisci-
plinary group of researchers working in areas close to HRI, and to
help identify the issues and challenges in HRI research. Although much
research had been done prior to this event, some consider it to be sem-
inal in the emergence of the field as its own discipline. A second NSF
workshop was held in 2006 [181].
In July 2004, IEEE-RAS and the International Foundation of
Robotics Research (IFRR) sponsored a summer school on “Human–
Robot Interaction.” This event brought together six experts from
the field of HRI and approximately 30 PhD students for a week in
Volterra Italy for four intensive days of lectures and events. A sim-
ilar event that has been held annually since 2004 is the Rescue
Robotics Camp (see, for example, [239, 240]). About the same time,
a series of special issues dedicated to HRI began to appear in journals
[5, 157, 171, 201, 261].
In 2005, the US National Research Council sponsored a work-
shop entitled “Interfaces for Ground and Air Military Robots” [64].
The workshop discussed emerging interface and autonomy themes that
could be used across multiple scales to support primarily remote inter-
action of humans and robots.
The Japan Association for the 2005 World Exposition conducted
a Robot Project at EXPO 2005 that featured a wide range of robots
[6]. Guide, cleaning, service, and assistive robots were among the many
robots that were featured.
Starting in 2006, the ACM International Conference on Human–
Robot Interaction was created to specifically address the multidis-
ciplinary aspects of HRI research. Reflecting this multidisciplinary
nature, the 2007 conference was co-sponsored by the ACM Special
Interest Group on Computer Human Interaction, the ACM Special
Interest Group on Artificial Intelligence, and the IEEE Robotics and
Automation Society (RAS), with co-technical sponsorship from AAAI,
the Human Factors and Ergonomics Society, and the IEEE Systems,
Man, and Cybernetics Society. Associated with the HRI conference is
a NSF-funded student workshop. Other conferences have had a strong
interest in HRI including the following: the Humanoid Robotics work-
shops; the IEEE International Workshop on Safety, Security, and Res-
cue Robotics; and the Performance Metrics for Intelligent Systems
In 2006, the European Land-Robot Trial (ELROB) was created to
“provide an overview of the European state-of-the-art in the field of
[Unmanned Ground Vehicles]” [87]. Such systems frequently included
robust user interfaces intended for field conditions in challenging
environments, such as those faced in military and first responder
Another big influence in the emergence of HRI has been compe-
titions. The two with the greatest impact have been (a) the AAAI
Robotics Competition and Exhibition and (b) the Robocup Search and
Rescue competition. The Sixth AAAI Robot Competition in 1997 had
the first competition specifically designed for HRI research called “Hors
d’Oeuvres Anyone?” The goal of the competition was for a robot to
serve snacks to attendees of the conference during the conference recep-
tion. This event was repeated in 1998. Starting in 1999, a new grand
challenge event was introduced. For this competition, a team’s robot
had to be dropped off at the front door of the conference venue and,
through interaction with people, find its way to the registration desk,
register for the conference, and then find its way at the correct time
to a place where it was to give a presentation. This task was designed
to be hard enough to take many years to accomplish, helping to drive
research (see, for example, [276]). In recent years this conference held
several general human-interaction events.
In some cases, an application domain has helped to draw the
field together. Three very influential areas are robot-assisted search
and rescue, assistive robots, and space exploration. Literature from
each of these domains is addressed further in a subsequent section.
Robot-assisted search and rescue has been a domain in which the
214 Emergence of HRI as a Field
robotics field has worked directly with the end users which, in this
case, consists of specially trained rescue personnel. The typical search
and rescue situation involves using a small robot to enter into a poten-
tially dangerous rubble pile to search for victims of a building col-
lapse. The robots are typically equipped with a video camera and
possibly chemical and temperature sensors, and may sometimes be
equipped with a manipulator with which they can alter the envi-
ronment. The goal is to quickly survey an area that would other-
wise be unsafe for a human searcher to enter, and gather information
about victim location and structural stability. Because of the inher-
ently unstructured nature of search and rescue domains, the interac-
tions between the human and the robot are very rich. Consequently,
many HRI issues are addressed in the problem, and several ongoing
competitions are held to encourage robotics researchers to participate
[159, 199, 325].
Assistive robot systems seek to provide physical, mental, or social
support to persons who could benefit from it such as the elderly or
disabled. Assistive robotics is important to HRI because it empha-
sizes proximate interaction with potentially disabled persons. HRI
challenges from this domain include providing safe physical contact
or moving within very close proximity. The challenges also include
supporting effective social interactions through cognitive and emo-
tive computing, and through natural interactions such as gesture and
speech. Although sometimes referred to by names other than robots, the
types of robots/machines used in assistive applications vary widely in
their physical appearance, and include wheelchairs, mobile robots with
manipulators, animal-like robots, and humanoids [90, 206, 246, 299].
Because of the close proximity and sometimes long-term interactions,
appropriate HRI in assistive robotics may be sensitive to cultural influ-
ences [152, 270].
Space robotics has also been an important domain for HRI because
of the challenges that arise under such extreme operating conditions.
These challenges include operating a remote robot when the time lag
can be a significant factor, or interacting in close proximity such as
when a robot assistant helps an astronaut in exploring the surface
of a planetary body. A typical anticipated situation is a geological
study that involves prolonged work on the surface of a planetary body,
possibly using specialized sensors such as ground-penetrating radar and
specialized manipulators such as a drill and hammer [84]. Information
gathered by the robot needs to be returned either (a) to an astronaut
who is co-located with the robot or (b) to a ground-bases science team
who then form real-time hypotheses that are used to modify the behav-
ior of the robot.
What Defines an HRI Problem?
The HRI problem is to understand and shape the interactions between
one or more humans and one or more robots. Interactions between
humans and robots are inherently present in all of robotics, even for
so called autonomous robots — after all, robots are still used by and
are doing work for humans. As a result, evaluating the capabilities of
humans and robots, and designing the technologies and training that
produce desirable interactions are essential components of HRI. Such
work is inherently interdisciplinary in nature, requiring contributions
from cognitive science, linguistics, and psychology; from engineering,
mathematics, and computer science; and from human factors engineer-
ing and design.
Although analysis of anticipated and existing interaction patterns
is essential, it is helpful to adopt the designer’s perspective by breaking
the HRI problem into its constituent parts. In essence, a designer can
affect five attributes that affect the interactions between humans and
Level and behavior of autonomy,
Nature of information exchange,
Structure of the team,
4.1 Autonomy 217
Adaptation, learning, and training of people and the
robot, and
Shape of the task.
Interaction, the process of working together to accomplish a goal,
emerges from the confluence of these factors. The designer attempts
to understand and shape the interaction itself, with the objective of
making the exchange between humans and robots beneficial in some
sense. We now discuss each of these attributes in detail, including ref-
erences from the literature.
4.1 Autonomy
Designing autonomy consists of mapping inputs from the environment
into actuator movements, representational schemas, or speech acts.
There are numerous formal definitions of autonomy and intelligence in
the literature [7, 20, 119, 184, 256], many of which arise in discussions
of adjustable or dynamic autonomy [30]. One operational characteriza-
tion of autonomy that applies to mobile robots is the amount of time
that a robot can be neglected, or the neglect tolerance of the robot [68].
A system with a high level of autonomy is one that can be neglected
for a long period of time without interaction. However, this notion of
autonomy does not encompass Turing-type notions of intelligence that
might be more applicable to representational or speech-act aspects of
Autonomy is not an end in itself in the field of HRI, but rather a
means to supporting productive interaction. Indeed, autonomy is only
useful insofar as it supports beneficial interaction between a human and
a robot. Consequently, the physical embodiment and type of autonomy
varies dramatically across robot platforms; see Figure 4.1, which shows
a cross section of the very many different types of physical robots.
Perhaps the most strongly human-centered application of the con-
cept of autonomy is in the notion of level of autonomy (LOA).Levels of
autonomy describe to what degree the robot can act on its own accord.
Although many descriptions of LOA have been seen in the literature,
the most widely cited description is by Tom Sheridan [269]. In Sheri-
dan’s scale, there is a continuum from the entity being completely con-
218 What Defines an HRI Problem?
Fig. 4.1 Representative types of robots. In clockwise order beginning in the upper left:
RepileeQ2 — an extremely sophisticated humanoid [136]; Robota — humanoid robots as
“educational toys” [21]; SonyAIBO — a popular robot dog ; (below the AIBO) A sophisti-
cated unmanned underwater vehicle [176]; Shakey — one of the first modern robots, cour-
tesy of SRI International, Menlo Park, CA [279]; Kismet — an anthropomorphic robot with
exaggerated emotion [65]; Raven — a mini-UAV used by the US military [186]; iCAT —
an emotive robot [REF]; iRobotPackBot— a robust ground robot used in military
applications [135]. (All images used with permission.)
trolled by a human (i.e., teleoperated), through the entity being com-
pletely autonomous and not requiring input or approval of its actions
from a human before taking actions:
1. Computer offers no assistance; human does it all.
2. Computer offers a complete set of action alternatives.
3. Computer narrows the selection down to a few choices.
4. Computer suggests a single action.
5. Computer executes that action if human approves.
6. Computer allows the human limited time to veto before auto-
matic execution.
7. Computer executes automatically then necessarily informs
the human.
8. Computer informs human after automatic execution only if
human asks.
4.1 Autonomy 219
9. Computer informs human after automatic execution only if
it decides too.
10. Computer decides everything and acts autonomously, ignor-
ing the human.
Variations of this scale have been developed and used by various authors
[144, 222]. Importantly, Miller and Parasuraman have noted that such
scales may not be applicable to an entire problem domain but are
rather most useful when applied to each subtask within a problem
domain [188]. The authors further suggest that previous scales actually
represent an average over all tasks.
While such (average) scales are appropriate to describe how
autonomous a robot is, from a human–robot interaction point of view,
a complementary way to consider autonomy is by describing to what
level the human and robot interact and the degree to which each is capa-
ble of autonomy. The scale presented in Figure 4.2 gives an emphasis
to mixed-initiative interaction, which has been defined as a “flexible
interaction strategy in which each agent (human and [robot]) con-
tributes what it is best suited at the most appropriate time” [122].
Various and different HRI issues arise along this scale. On the direct
control side, the issues tend toward making a user interface that reduces
the cognitive load of the operator. On the other extreme of peer-
to-peer collaboration, issues arise in how to create robots with the
appropriate cognitive skills to interact naturally or efficiently with
a human.
Note that in order for the robot to achieve peer-to-peer collabo-
ration, it must indeed be able to flexibly exhibit “full autonomy” at
appropriate times. Moreover, it may need to support social interactions.
Fig. 4.2 Levels of autonomy with emphasis on human interaction.
220 What Defines an HRI Problem?
As a result, peer-to-peer collaboration may be considered more difficult
to achieve than full autonomy.
Autonomy is implemented using techniques from control theory,
artificial intelligence, signal processing, cognitive science, and linguis-
tics. A common autonomy approach is sometimes referred to as the
sense-plan-act model of decision-making [196]. This model has been a
target of criticism [39] and sometimes rightfully so, but much of the
criticism may be a function of the early capacities of robots such as
Shakey [209] rather than failings of the model per se. This model is
typified by artificial intelligence techniques, such as logics and planning
algorithms [253]. The model can also incorporate control theoretic con-
cepts, which have been used very successfully in aviation, aeronautics,
missile control, and etc. (see, for example, [175]).
In the mid 1980s, Brooks, Arkin, and others revolutionized the field
of robotics by introducing a new autonomy paradigm that came to be
known as behavior-based robotics. In this paradigm, behavior is gen-
erated from a set of carefully designed autonomy modules that are
then integrated to create an emergent system [10, 38, 40]. These mod-
ules generate reactive behaviors that map sensors directly to actions,
sometimes with no intervening internal representations. This model for
behavior generation was accompanied by hardware development that
allowed autonomy modules to be implemented in the small form factors
required for many robotics applications.
Today, many researchers build sense-think-act models on top of a
behavior-based substrate to create hybrid architectures [196]. In these
systems, the low-level reactivity is separated from higher level reason-
ing about plans and goals [28]. Some have developed mathematics and
frameworks that can be viewed as formalizations of hybrid architectures
and which are referred to as theories of intelligent control [7, 255]. Inter-
estingly, some of the most challenging problems in developing (hybrid)
behaviors is in producing natural and robust activity for a humanoid
robot [194, 273, 323].
Complementing the advancement of robotic control algorithms has
been the advancement of sensors, sensor-processing, and reasoning algo-
rithms. This is best represented by the success of the field of prob-
abilistic robotics, typified by probabilistic algorithms for localization
4.2 Information Exchange 221
and mapping [161, 289]. It is no overstatement to say that these
algorithms, which frequently exploit data from laser and other range
finder devices, have allowed autonomy to become truly useful for
mobile robots [290], especially those that require remote interaction
through periods of autonomous behavior and autonomous path plan-
ning [42, 276, 284, 291, 293]. Although probabilistic algorithms can be
computationally expensive, the memory capacity, computational speed,
and form factor of modern computers have allowed these algorithms to
be deployable.
The areas of representing knowledge and performing reasoning,
especially in team contexts, have also grown. Example developments
include the emergence of belief-desire-intention architectures [321],
joint intention theory [60], affect-based computing [31, 223, 229], and
temporal logics.
4.2 Information Exchange
Autonomy is only one of the components required to make an inter-
action beneficial. A second component is the manner in which infor-
mation is exchanged between the human and the robot (Figure 4.3).
Measures of the efficiency of an interaction include the interaction
time required for intent and/or instructions to be communicated to
the robot [68], the cognitive or mental workload of an interaction [268],
the amount of situation awareness produced by the interaction [88] (or
reduced because of interruptions from the robot), and the amount of
shared understanding or common ground between humans and robots
[143, 160].
There are two primary dimensions that determine the way informa-
tion is exchanged between a human and a robot: the communications
medium and the format of the communications. The primary media are
delineated by three of the five senses: seeing, hearing, and touch. These
media are manifested in HRI as follows:
visual displays, typically presented as graphical user inter-
faces or augmented reality interfaces [15, 145, 154, 208],
gestures, including hand and facial movements and by
movement-based signaling of intent [31, 73, 247, 305],
222 What Defines an HRI Problem?
Fig. 4.3 Types of human–robot interaction. Counterclockwise from top left: haptic robot
interaction from Georgia Tech [102], a “physical icon” for flying a UAV from Brigham
Young University, peer-to-peer interaction with the robot Kaspar from the University
of Hertfordshire [298], teleoperation of NASA’s Robonaut [205], a PDA-based interface
for flying a UAV from Brigham Young University, gesture- and speech-based interac-
tion with MIT’s Leonardo [189], a touchscreen interaction with a Cogniron robot [169],
and (center) physical interaction with the RI-MAN robot [24]. (All images used with
speech and natural language, which include both audi-
tory speech and text-based responses, and which fre-
quently emphasize dialog and mixed-initiative interaction
[126, 227],
non-speech audio, frequently used in alerting [78], and
physical interaction and haptics, frequently used remotely
in augmented reality or in teleoperation to invoke a sense
of presence especially in telemanipulation tasks [10, 282],
and also frequently used proximately to promote emotional,
social, and assistive exchanges [56, 124, 172, 272].
4.3 Teams 223
Recently, attention has focused on building multimodal interfaces [226],
partly motivated by a quest to reduce workload in accordance to Wick-
ens’ multiple resource theory [314] and partly motivated by a desire to
make interactions more natural and easier to learn [248, 254, 281].
The format of the information exchange varies widely across
domains. Speech- and natural language-based exchanges can be
scripted and based on a formal language, can attempt to support full
natural language, or can restrict natural language to a subset of lan-
guage and a restricted domain (see, for example, [52, 120, 251, 275,
276]). Importantly, speech-based exchanges must not only address the
content of information exchanged, but also the rules of such exchange
al´a the Gricean maxims [118], which ask to what extent the speech
is truthful, relevant, clear, and informative. Haptic information pre-
sentation can include giving warnings through vibrations, promoting
the feeling of telepresence, supporting spatial awareness through hap-
tic vests, and communicating specific pieces of information through
haptic icons (see, for example, [53, 164, 235]). Audio information
presentation can include auditory alerts, speech-based information
exchange, and 3D awareness (see, for example, [285]). Presenting social
information can include attentional cueing, gestures, sharing physical
space, imitation, sounds, facial expression, speech and natural language
[22, 35, 36, 83, 94, 203, 149, 260]. Finally, graphical user interfaces
present information in ways that include ecological displays, immer-
sive virtual reality, and traditional windows-type interactions (see, for
example, [9, 15, 185, 208]).
4.3 Teams
HRI problems are not restricted to a single human and a single robot,
though this is certainly one important type of interaction. Robots used
in search and rescue, for example, are typically managed by two or more
people, each with special roles in the team [197, 264]. Similarly, manag-
ing Unmanned/Uninhabited Air Vehicles (UAVs) is typically performed
by at least two people: a “pilot,” who is responsible for navigation and
control, and a sensor/payload operator, who is responsible for manag-
ing cameras, sensors, and other payloads [82, 182].
224 What Defines an HRI Problem?
A question that has received considerable attention, but which is
directly addressed by few scientific studies, is how many remote robots
a single human can manage. In general, the answer is dependent on
factors such as the level of autonomy of the robot (e.g., teleoperation
requires a great deal of direct attention from the human), the task
(which defines not only the type and quantity of data being returned
to the human but also the amount of attention and cognitive load
required of the human), and the available modes of communication.
In the search and rescue domain, Murphy [197] asserts that the
demands of the task, the form factor of the robot, and the need to pro-
tect robot operators requires at least two operators, an observation that
has received strong support from field trials using mature technologies
[45], and partial support in search and rescue competitions using less
mature but more ambitious technologies [264]. In other domains, some
assert that, given sophisticated enough autonomy and possibly coordi-
nated control, it is possible for a single human to manage more than one
robotic asset [187, 190] though the task may still need another human
to interpret sensor information. Still others assert that this problem is
ill-formed when robots are used primarily as an information-gathering
tool [121]. An intermediate position is that the right question should
not focus on how many robots can be managed by a single human, but
rather the following: how many humans does it take to efficiently man-
age a fixed number of robots, allowing for the possibility of adaptable
autonomy and dynamic handoffs between humans [266].
One measure that has received some attention in the literature is
the notion of fan-out, which represents an upper bound on the number
of independent, homogeneous robots that a single person can manage
[216, 217]. This measure is supported by a limited set of techniques
for estimating it [68]. Some work has been done to refine the fan-out
to apply to teams of heterogeneous robots [112] and to tighten the
bound by identifying various aspects of interaction [190]. In its present
form, however, it is clear that fan-out is only a designer guideline and is
insufficient, for example, to provide a trigger strategy [144] for adaptive
automation. Alternatives to fan-out include predicting the performance
of a team of heterogeneous robots from measurements of neglect toler-
ance and interaction times [69].
4.3 Teams 225
In addition to the number of humans and robots in a team, a key
problem is the organization of the team [98, 213]. One important orga-
nizational question is who has the authority to make certain decisions:
robot, interface software, or human? Another important question is
who has the authority to issue instructions or commands to the robot
and at what level: strategic, tactical, or operational? A third important
question is how conflicts are resolved, especially when robots are placed
in peer-like relationships with multiple humans. A fourth question is
how roles are defined and supported: is the robot a peer, an assistant,
or a slave; does it report to another robot, to a human, or is it fully
Spanning all of these questions is whether the organizational struc-
ture is static or dynamic, with changes in responsibilities, authorities,
and roles. In one study, managing multiple robots in a search and rescue
domain under either manual or coordinated control produced results
that strongly favored coordinated control [308]. In another study, four
autonomy configurations, including two variations of sliding autonomy,
were managed by a human working on a construction task with a team
of heterogeneous robots [266]. In this study, the tradeoffs between time
to completion, quality of behavior, and operator workload were strongly
evident. This result emphasizes the importance of using dynamic auton-
omy when the world is complex and varies over time. In a third study,
researchers explored how making coordination between robots explicit
can reduce failures and improve consistency, in contrast to traditional
interfaces [147]. In a fourth study, researchers explored the minimal
amount of gestural information required to command various forma-
tions to a team of robots [277].
In many existing and envisioned problems, HRI will include not only
humans and robots interacting with each other, but also coordinating
with software agents. The most simple form of this is a three-agent
problem which occurs when an intelligent interface is the intermediary
between a human and a remote robot [249]. In this problem, the inter-
face agent can monitor and categorize human behavior, monitor and
detect problems with the robot, and support the human when workload
levels, environment conditions, and robot capabilities change. A more
complicated form of this teaming is in anticipated NASA applications
226 What Defines an HRI Problem?
where multiple distributed humans will interact with robots and with
software agents that coordinate mission plans, human activities, and
system resources [29].
A final issue that is starting to gain attention is the role of the
human [262]. While much of the discussion up to this point is with
respect to humans and robots performing a task together, there are
cases where the robot may have to interact with bystanders or with
people who are not expecting to work with a robot. Examples include
the urban search and rescue robot that comes across a human to be
rescued, a military robot in an urban environment that must interact
with civilians, and a health assistant robot that must help a patient
and interact with visitors. The role of the robot with respect to humans
must be taken into account. The role of the human will be discussed in
more detail in Section 5.
4.4 Adaptation, Learning, and Training
Although robot adaptation and learning have been addressed by many
researchers, training of humans appears to have received comparatively
little attention in the HRI literature, even though this area is very
important. One reason for this apparent trend is that an often unstated
goal of HRI is to produce systems that do not require significant train-
ing. This may be because many robot systems are designed to be used
in very specific domains for brief periods of times [271, 292]. Moreover,
robot learning and adaptation are often treated as useful in behavior
design and in task-specific learning, though adaptation is certainly a
key element of long-term interactions between humans and robots [104].
On one hand, it is important to minimize the amount of human
training and adaptation required to interact with robots that are used
in therapeutic or educational roles for children, autistic individuals, or
mentally challenged individuals. On the other hand, it is important
that HRI include proper training for problems that include, for exam-
ple, handling hazardous materials; similarly the very nature of using
robots in therapeutic and educational roles requires that humans should
directly adapt and learn from the interaction [148]. In this section, we
discuss not only HRI domains that require minimum operator training,
4.4 Adaptation, Learning, and Training 227
but also domains that require careful training. We also discuss efforts
aimed to train HRI scientists and designers, and then conclude with a
discussion of how the concept of training can be used to help robots
evolve new skills in new application domains.
Minimizing Operator Training. Minimizing training appears to
be an implicit goal for “edutainment” robots, which include robots
designed for use in classrooms and museums, for personal entertain-
ment, and for home use. These robots are typically designed to be
manageable by a wide variety of humans, and training can range from
instruction manuals, instruction from a researcher, or instructions from
the robot itself [210, 275]. One relevant study explored how ROOMBA
robots are used in practice without attempting to make operators use
the robots in a specific way [99]. Such studies are important because
they can be used to create training materials that guide expectations
and alert humans to possible dangers. Other such studies include those
that explore how children use education robots in classroom settings
[148], investigate how disabled children interact with robots in social
settings [23], support humans in the house [302], and identify interac-
tion patterns with museum guide robots [210].
Complementing such studies are efforts to use archetype patterns of
behavior and well-known metaphors that trigger correct mental mod-
els of robot operation. Examples include the often stated hypotheses
that people with “gaming experience” will be able to interact better
(in some sense) with mobile robots than those with limited experiences
in games [241]. We are not aware of any studies that directly support
this hypothesis, but if it is true then it would seem to suggest that
people with experiences in video-conferencing, instant-messaging, and
other computer-mediated forms of communication might more natu-
rally interact with robots. Whether this hypothesis is true is a matter
of future work, but it is almost certainly true that such experiences help
people form mental models that influence interactions [238]. Designers
are seeking (a) to identify interaction modes that invoke commonly held
mental models [66] such as those invoked by anthropomorphic robots
[156] or (b) to exploit fundamental cognitive, social, and emotional
processes [32]. One possible caution for these efforts is that robots may
228 What Defines an HRI Problem?
reach an “uncanny valley” where expectations evoked by the robot fall
short of actual behavior producing an interaction that can feel strangely
uncomfortable to humans [71, 194]. However, this uncanny valley the-
ory is unproven although researchers are now trying to experimentally
verify its existence [179].
Efforts to Train Humans. In contrast to the goal of minimizing
training in edutainment robots, some application domains involving
remote robots require careful training because operator workload or
risk is so high. Important examples of such training are found in mil-
itary and police applications, space applications, and in search and
rescue applications. Training for military and police applications is
typified by “bomb squad” robots, training for space applications is
typified by telemanipulation tasks [234], and training for military and
civilian search and rescue is typified by reconnaissance using small,
“human-packable robots” [85]. In both the military and search applica-
tion domains, training efforts exist for both air and ground robots, and
these efforts tend to emphasize the use of mobile robots in a mission
context [87]. Training efforts include instructions on using the inter-
face, interpreting video, controlling the robot, coordinating with other
members of the team, and staying safe while operating the robot in
a hostile environment. Such training is often given to people who are
already experts in their fields (such as in search and rescue), but is also
given to people who may be relatively inexperienced. In the military,
police and space domains, training programs may be complemented by
selection criteria to help determine which indviduals are likely to be
better (in some sense) at managing a robot [79]. Selection appears to
have received more attention in air robots than ground robots.
By contrast to interactions with remote robots, many applica-
tions involving proximate robots are designed to produce learning or
behavioral responses with humans. Therapeutic and social robots are
designed to change, educate, or train people, especially in long-term
interactions [148, 245, 312]. People also adapt to service robots over the
long-term and over a wide range of tasks [115], and there is growing
evidence that many long-term interactions require mutual adaptation
including with human bystanders [75, 131, 117]. Importantly, culture
4.4 Adaptation, Learning, and Training 229
appears to influence both long-term and short-term adaptation, at least
as far as accepting interactions with a robot [18, 94, 127, 150, 270, 306].
Training Designers. Importantly, an often overlooked area is the
training of HRI researchers and designers in the procedures and
practices of those whom they seek to help. Important examples of
training researchers include Murphy’s workshops on search and res-
cue robotics [198], tutorials and workshops on methodologies for
understanding a work-practice domain and field studies [58], tuto-
rials for young researchers on search and rescue [239, 240], and
tutorials and workshops on metrics or experiment design for robot
applications [294].
Training Robots. It is tempting to restrict training to the education
of the human side of HRI, but this would be a mistake given current
HRI research. In HRI, robots are also learning, both offline as part of
the design process [37, 207] and online as part of interaction, especially
long-term interaction [89, 251]. Such learning includes improving per-
ceptual capabilities through efficient communication between humans
and robots [33, 89, 251, 330], improving reasoning and planning capa-
bilities through interaction [295, 34], and improving autonomous capa-
bilities [258]. Approaches to robot learning include teaching or pro-
gramming by demonstration [13, 55, 76, 77, 146, 218], task learning
[34, 251, 207], and skill learning including social, cognitive, and loco-
motion skills [11, 22, 202, 233, 295]. Some researchers are exploring
biologically inspired learning models, including how teaching among
humans or social animals can be used to train a robot [11, 257];
others are exploring how learning can become more efficient if it
leverages information about how the human brain learns in very few
trials [100].
Interestingly, it can be argued that providing support for effi-
cient programming or knowledge management systems is an impor-
tant aspect of training robots in HRI [120, 327]. Additionally, it can
be argued that sensitizing a robot to issues of culture and etiquette
allows them to adapt to slowly changing human norms of behavior
[141, 215, 252].
230 What Defines an HRI Problem?
4.5 Task-Shaping
Robotic technology is introduced to a domain either to allow a human
to do a task that they could not do before, or to make the task
easier or more pleasant for the human. Implicit in this assertion is
the fact that introducing technology fundamentally changes the way
that humans do the task. Task-shaping is a term that emphasizes
the importance of considering how the task should be done and will
be done when new technology is introduced. Compared to the other
ways that a designer can shape HRI, there is little written about
There are formal processes for understanding how the task should
be done and is currently done. These processes include goal-directed
task analyses, cognitive work analyses, and ethnographic studies [58,
88, 301]. Although frequently used to specify how a task is done and
how it should be done, it is imperative to consider how the task will be
done, including unintended consequences of design [14, 212].
One reason that little is written about task-shaping is because
designers are implicitly trying to create technology and interactions
that accomplish some task or function. Indeed, Woods has persuasively
argued that designing a system is equivalent to making a hypothesis
about how the artifact will positively shape the experience [319]. Nev-
ertheless, it is important to consider how the task might be modified to
better support interaction. Examples of explicit task-shaping include
designing space or underwater equipment and tools so that handles
and connectors can be manipulated by a robotic arm, “pre-cleaning” a
room so that a robot vacuum can accomplish its task most efficiently
[99], and performing pre-inspection tasks used to form maps and plans
that can be executed by a robot.
4.6 Finding a Unifying Theme
There are multiple ways to frame HRI as a field. One approach is
to treat HRI as a resurgent emphasis and extension of previous work
in human factors, teleoperation, and supervisory control. Another
approach to framing HRI is to view it as a new field that includes a con-
4.6 Finding a Unifying Theme 231
vergence of previous work with new research problems caused by some
new capability that fundamentally changes the problem. We assert that
robot autonomy has reached the point where mixed-initiative interac-
tion and semi-autonomous control have fundamentally changed the field
from previous research on related problems. Thus, we treat HRI as a
new field that faces opportunities and problems which are not simple
extensions of previous work. We acknowledge, however, that it is pos-
sible to make persuasive arguments that HRI is simply a refocusing of
previous efforts rather than a new field.
One way to unify the scope of current HRI research is to con-
dense the five dimensions of designer influence into a single concept
as exemplified in our proposed scale of interaction, Figure 4.2, with the
caveat that this single concept cannot capture every nuance and possi-
ble design of every HRI problem. The concept of dynamic interaction
seems to capture the current research direction of many HRI efforts.
Dynamic interaction includes time- and task-varying changes in
autonomy, information exchange, team organization and authority,
and training. It applies to both remote and proximate interactions,
including social and physical interactions. By including variable auton-
omy assignments, the concept of dynamic interaction subsumes adap-
tive and dynamic autonomy as a special case [80, 96, 97, 111, 144,
165, 259]. By including information exchange, dynamic interaction
includes adaptive and adaptable interfaces [144, 153, 221]. By includ-
ing team organization and authority, mixed initiative interaction [43,
98, 162, 226] is addressed. By including training, interactive learning
is included.
More importantly, the concept of dynamic interaction places the
emphasis on shaping the types of interactions that can and will
emerge as humans and robots interact. The scope of HRI research
and design, therefore, includes all efforts at evaluating systems and
interaction paradigms, designing autonomy algorithms in the con-
text of HRI, designing interfaces and information exchange protocols,
defining and switching roles, and influencing learning and training.
This emphasis on dynamic interaction differs sharply from the histor-
ically static interactions of pure teleoperation and pure supervisory
232 What Defines an HRI Problem?
Note that some current research efforts and methods do not natu-
rally fit into the dynamic interaction framework. These include several
aspects of task shaping, including ethnographic studies, goal-directed
task analyses, and some cognitive science-based work. However, under-
standing existing processes and potential use patterns helps researchers
better understand the fluid interaction patterns that are likely to exist
in practice so that they can design interactions that support, improve,
and extend these interaction patterns.
Problem Domains in HRI
We have already mentioned several of the problem domains and appli-
cation areas in modern HRI. In this section, we elaborate on many of
these problem domains to present a survey of the kinds of problems
encountered in HRI. Importantly, many of these problems have broad
social impact; thus, much work in HRI uses science and engineering to
respond to needs in society.
Scholtz provided a taxonomy of roles that robots can assume in
HRI [262]:
Peer, and
To this list, we add the following:
Mentor: the robot is in a teaching or leadership role for the
human, and
234 Problem Domains in HRI
Information Consumer: the human does not control the
robot, but the human uses information coming from the
robot in, for example, a reconnaissance task.
Similar taxonomies are certainly possible, but identifying how people
perceive a robot’s role has important ramifications for how they inter-
act with the robot [74]. Using Scholtz’s taxonomy provides insight into
the current and future interactions in these applications. Table 5.1 clas-
sifies the most frequent types of interactions for the application areas
discussed in the remainder of this section. For many of these areas, cur-
rent research patterns exhibit a trend away from remote interactions
Table 5.1 Examples of roles and proximity patterns that arise in several application areas.
Application area Remote/
Role Example
Search and rescue Remote Human is supervisor or
Remotely operated search
Proximate Human and robot are
Robot supports unstable
Assistive robotics Proximate Human and robot are
peers, or robot is tool
Assistance for the blind,
and therapy for the
Proximate Robot is mentor Social interaction for
autistic children
Military and police Remote Human is supervisor Reconnaissance,
Remote or
Human and robot are
Patrol support
Remote Human is information
Commander using
Edutainment Proximate Robot is mentor Robotic classroom
Robot is mentor Robotic museum tour
Robot is peer Social companion
Space Remote Human is supervisor or
Remote science and
Proximate Human and robot are
Robotic astronaut
Home and industry Proximate Human and robot are
Robotic companion
Proximate Human is supervisor Robotic vacuum
Remote Human is supervisor Robot construction
5.1 Search and Rescue 235
toward proximate interactions, and away from operator roles toward
peer or mentor roles.
5.1 Search and Rescue
The highest profile HRI research area in the United States is a strong
example of work with important social impact. This area is urban
search and rescue (USAR), and is exemplified by the use of robots
in rescue and recovery efforts after the collapse of the World Trade
Center buildings [51]. Lead by pioneering government and academic
efforts [26, 200], USAR has grown into one of the most important areas
of HRI.
Because of its importance, USAR has become an HRI challenge
problem. As such, there have been efforts to provide standardized
USAR test areas and performance measures, and to standardize
robot-assisted USAR efforts [138, 199, 263]. There are regular USAR
competitions at robot-related conferences [159, 199, 325]. A wide vari-
ety of interface concepts, autonomy designs, sensor-processing algo-
rithms, robot morphologies, field studies, and human factors analyses
and experiments have been created in the name of robot-assisted USAR
(see, for example, [197, 198, 211, 264, 318]). Recently, these efforts are
being extended from ground robots to include aerial robots used in
natural disaster and wilderness search [61, 278].
5.2 Assistive and Educational Robotics
In the spirit of socially relevant research, robots are being developed
to serve in assistive and educational capacities. Assistive robotics is
perhaps one of the highest profile areas of HRI in the world. This
application domain often places the robot in a peer-like or mentoring
role with the human in practice, even though the intention of the robot
is designed to provide service to the human. For example, robots being
developed to assist the visually challenged work in close proximity to
humans and must merit an appropriate level of trust. The goal of work
in this area is to increase the set of tasks that a visually impaired per-
son can independently perform. These tasks include providing naviga-
236 Problem Domains in HRI
tion assistance in unstructured domains [167, 170, 274], and providing
information about locations (and prices) in grocery stores and trans-
portation facilities [168].
Another important area of research, especially in countries with a
burgeoning population of the elderly, is providing support for those
who have age-related challenges [192, 231, 232, 252, 304]. Researchers
in this area focus on both physical needs such as mobility assistance
[324], emotional welfare [252, 303], and cognitive assistance [151]. Many
working in this area are fully aware of the ethical considerations that
arise by delegating a companionship role to a robot and the trust issues
that arise by having an artificial agent working with someone with
mental challenges such a senility [286]. Having the ability to remotely
manage a personal assistant robot over the internet may be a step in
the direction of addressing these ethical concerns, though substantial
technological and privacy limitations exist for such interaction [166].
For some people with physical and mental challenges, robots may
provide an opportunity for interaction and therapy. Such work is being
explored with autistic children [23, 247]. Many of these children respond
weakly or not at all to social cues, but respond well to mechanical
devices. Robots provide a possible therapeutic role for using a mechan-
ical device to improve social interactions [312]. Robots are also being
considered for other domains where children are benefited, such as those
who have experienced trauma. Importantly, the social dimension of HRI
is considered necessary not only in assistive roles but also in many areas
of proximate interaction [31, 32, 72, 90, 106, 148]. Indeed, the area of
social robotics is so large that it is already time to update the highly
cited 2003 survey [94].
For some people with physical challenges, the embodiment of a robot
provides unique opportunities not available in other forms of technol-
ogy. For example, researchers are working on designing robots that pro-
vide support for physical therapy. Efforts include providing prescribed
force and movement trajectories to help rebuild flexibility and strength
[178]. Other work includes detecting motivational state and adjusting
therapy to maximize benefits [236]. Intelligent wheelchairs are a type
of robot that uses external sensors to support path planning, collision
avoidance, etc. for a person that requires a wheelchair [134, 324].
5.3 Entertainment 237
Physical interaction with robots is not limited to providing assis-
tance to those with some form of disability. Many robot architectures
have been designed to assist humans in industrial settings [228]. Indeed,
developing telerobots for use in hazardous materials handling has been
ongoing for several decades [139]. However, cooperative systems are
not limited to large-scale industrial settings, but may be most useful in
small-scale tasks “such as microassembly and microsurgery” [180]. The
technologies developed for such assistive support appear to apply not
only to proximate physical interaction, but also to the general telema-
nipulation problem [224].
In the spirit of providing assistance to those without a disability,
some researchers are also exploring how robots can be used to promote
education for typical children, both in the home and in schools [62, 140].
The personal service robot is an extension to the theme of providing
service to the general population. Indeed, South Korea has ambitious
goals about the advancement of such robots [219].
5.3 Entertainment
Although there have been many examples of entertainment robotics,
from an HRI perspective, not much has been published in the liter-
ature. Early entertainment robotics centered on animatronics, where
the robot generally plays prerecorded sounds that are synchronized
with the robots motion. These types of robots can often be found in
old movies and theme parks; however, the interaction is mostly in one
direction, that of the robot presenting information, although the robot’s
performance may be triggered by the presence of the human. However,
the 2005 AICHI Expo demonstrated several robots designed to enter-
tain, including the use of robots as actors and dance partners [6]; similar
work on the relationship between acting, drama, and artificial agents is
presented in [41]. Here again, the role of the human is as an observer,
and the interaction is minimal and more implicit [41].
The Valerie and Tank robots at Carnegie Mellon University were
designed as robot receptionists as a joint project between CMU’s
Robotics Institute and the School of Drama [106]. The robots have a
rich back story and strive to increase users’ interaction by encouraging
238 Problem Domains in HRI
the user to ask about the robots’ lives outside of their role as recep-
tionists. Although robots that act as tour guides might be considered
a form of assistive robotics, we classify such robots under the category
of entertainment because their primary role is to engage participants
[120, 210].
Although not strictly entertainment, the Insect Telepresence project
[8] placed Madagascar Hissing Cockroaches in a terrarium, and allowed
users, through mediated telepresence, to drive a miniature camera
within the terrarium, at eye level to the cockroaches. The image from
the camera was projected much larger than life on a wall. Of interest
was the mediated teleoperation, which limited the accelerations of the
camera motions to below that which would cause the cockroaches to
react to the camera, offering the humans a look into the cockroach’s
Other HRI-related research in the use of robots for entertainment
include robotic story tellers [191], robotic dance partners [163], robotic
plants that give users information such as incoming email [137], and
robotic pets [94, 272].
5.4 Military and Police
Many have called for robots to be used in tasks that are “dull, dirty,
or dangerous.” Military and police applications often simultaneously
satisfy all three criteria for robot use. Applications include gathering
information to support a dangerous task such as a SWAT team take-
down, or using remote vehicles in combat to minimize risk exposure
to soldiers. Current work emphasizes robots as servants to soldiers and
officers [87], typically in remote operations, but efforts to have robots
work in a peer-like role are underway [44, 155].
A typical use of robots in both military and police applications is
in bomb disposal (called “improvised explosive devices” in many mil-
itary situations) [263]. Remotely controlled robots are frequently used
to approach and evaluate suspicious packages [311]. Controlling these
robots is demanding on the operators, especially since many current
interfaces and autonomy levels require operators to integrate informa-
tion from multiple sources of data such as multiple cameras. In addition
5.5 Space Exploration 239
to controlling mobile platforms, telemanipulation is an important part
of such work. Robot arms are often mounted on the platforms providing
operators some ability to manipulate the object. Because of the lim-
ited situation awareness for controlling these arms, some researchers are
exploring sensor technologies and information presentation techniques
that improve awareness in telemanipulation [242].
5.5 Space Exploration
Robots have long been part of space exploration. In fact, according to
some definitions, a satellite can be considered a type of robot, albeit
one that has a high degree of autonomy and typically requires minimal
As has been previously noted, remarkable success in space robotics
include the Soviet Lunokhods which were used to explore the surface
of the moon [95] followed by more recent NASA success in exploring
the surface of Mars [174, 317]. It is anticipated that the robots will
continue to have a strong role in envisioned and future explorations of
the lunar and Martian surfaces, in construction tasks on these surfaces
as well as in the international space station, and in remote science and
maintenance tasks [84, 129, 177, 225].
Many precursor and early human missions will depend heavily on
remotely managed robots, but will also likely include extravehicular
activities. To prepare for these missions, NASA and other international
space agencies engage in frequent field work designed to evaluate both
the robotic and HRI technologies. Examples of this include develop-
ment of the Dante robots [16, 17], a series of Extra-Vehicular Activ-
ities (EVA)-based field tests [47, 114, 296], and field tests involving
substantial communications delays and remote science in harsh envi-
ronments [283, 310]. Research that extends these efforts includes the
aforementioned Robonaut development and work on developing intel-
ligent rovers and robot “mules” to pack astronaut equipment on long
duration EVAs.
Complementing the focus on remote and EVA-based interaction
are efforts to support astronauts at the international space station
and on long duration space missions. These efforts focus on astronaut
240 Problem Domains in HRI
assistants and small satellites [54, 81]. Many of the NASA programs
include strong planning components and seek to integrate software,
robotic, and human agents [30]. Other work calls for standardization
of robot parts, procedures, communications, and interfaces [91, 92].
5.6 UAV Reconnaissance and UUV Applications
Unmanned Air Vehicles are rapidly attracting attention as an applica-
tion area for HRI and for aviation technology. UAVs have been around
for a long time and have a large body of related literature, but the label
for such vehicles varies over time. Among other labels, UAV work has
been published under names like remotely piloted vehicles, uninhabited
air vehicles, autonomous micro air vehicles, and autonomous aerial sys-
tems. The key defining aspect of UAV interaction is the fact that the
vehicles are, by definition, remotely operated, move in three dimen-
sions, and typically have six degrees of freedom. Interestingly, remote
interactions and a high degree of freedom are also defining attributes
for problems that use Unmanned Underwater Vehicles (UUVs), often
referred to as autonomous underwater vehicles. Because of this simi-
larity, we unite our discussion of these two important areas of HRI.
This unification may be particularly relevant because the literature
from these two communities rarely overlaps even though the interac-
tion dynamics have marked similarities.
In addition to the previously mentioned reconnaissance-based mili-
tary and search-and-rescue uses of UAVs, there are a number of other
envisioned uses of these vehicles. These include atmospheric science
[107], landing site surveillance of a Mars rover [19], border patrol [103],
pollution monitoring [113], forest fire monitoring [49], infrastructure
inspection [113], and munitions-based military applications [70]. UUVs
have similarly broad areas of use. They include undersea science, trea-
sure hunting, undersea and surf-zone de-mining, and underwater con-
struction and maintenance [329]. For remote operations, there are many
interface designs that apply to both UAV and UUV operations. These
include integrating multiple perspectives, developing a sense of telep-
resence, synchronizing frames of reference through tethers, and building
mosaics [101, 230].
5.7 Other Applications 241
5.7 Other Applications
There are a host of other application domains where robots are used.
These domains include home use, manufacturing, inventory manage-
ment, mining, and precision agriculture. Importantly, some of these
application domains include strong HRI research, but others attempt
the creation of “fully autonomous” robots that do not require human
interaction and, ironically, are not “autonomous enough” to allow inter-
action with humans. Although it is possible that some of these domains
might allow “fire-up and forget” robots, it is likely that the usefulness
and safety of robots in many of these domains will increase if HRI
considerations are included in their design.
Solution Themes, Scientific Approaches,
and Challenge Problems
One measure of the maturity of a research field is the emergence of
a series of accepted practices and challenge problems that focus the
attention of the field. Equally important is the identification of solu-
tion themes that cross applications. In this section, we survey several
practices, challenge problems, and solution themes.
6.1 Accepted Practices
There are a number of accepted practices that are emerging in HRI.
A key practice is to include experts from multiple disciplines on research
efforts. These disciplines frequently include robotics, electrical and
mechanical engineering, computer science, human–computer interac-
tion, cognitive science, and human factors engineering. Other relevant
disciplines include design, organizational behavior, and the social sci-
ences. Importantly, some conferences encourage multidisciplinary sub-
missions are working to establish the practice of having all papers ref-
ereed by reviewers representing different disciplines [265].
A second emerging practice is to create real systems (robot auton-
omy, interaction modes, and etc.) and then evaluate these systems
6.1 Accepted Practices 243
using experiments with human subjects. Proof-of-concept technologies,
although important, are less valuable than they would be if they were
supported by careful experiments that identify key attributes of the
design or principles that span applications. Identification of descrip-
tive interaction phenomena is interesting, but elaboration on the psy-
chological principles underlying these phenomena with an eye toward
harnessing these principles in design is more useful. Thus, engineering,
evaluation, and modeling are key aspects of HRI.
A third emerging practice is conducting experiments that include
a careful blending of results from simulated and physical robots. On
the one hand, because of cost and reliability issues, it is often difficult
to conduct carefully controlled experiments with physical robots. On
the other hand, it is often difficult to replicate simulation-only results
with physical robots because the physical world presents challenges
and details that are not present in many simulations. It is common
to “embody” at least one portion of the interaction, be it a physi-
cal robot, some physical sensor, or real-world speech. Some research
includes work using carefully controlled simulation environments and
replication of selected results with physical robots. Others use wizard
of Oz studies. Others form communities where roboticists design tech-
nologies and where other human factors researchers collect and analyze
results from tests; this is the operating structure of part of the robot-
assisted urban search and rescue (USAR) community [325]. Interest-
ingly, at least one research group is exploring how a simulated user can
help support the design of human–robot interfaces [244].
A fourth area of emerging effort is establishing standards and com-
mon metrics. The most complete survey of metrics is in [280], but
much work on metrics exists in the literature including the proceedings
of the annual PERMIS workshops. Standardization efforts have been
strongest in the USAR domain [138, 199], but are also present in space
applications and UAVs [91, 92].
A fifth emerging practice is the use of longitudinal studies. Such
studies, which can last from several weeks to several months, require a
considerable investment by researchers, both in terms of hours and
financial resources. One reason that long-term studies are a rela-
tively recent practice is that many robots were not reliable enough
244 Solution Themes, Scientific Approaches, and Challenge Problems
to work over the study period. The availability of reliable per-
sonal home robots and service robots in public areas has made such
studies possible [99, 120]. The European COGNIRON project is a
good example of a commitment to long-term studies [59]. Long-
term studies shift research methodologies from carefully controlled
small-scale experiments to other methodologies such as surveys and
6.2 Challenge Problems
It is often useful to identify a set of challenge problems that focus the
efforts of a community. HRI has a suite of challenge problems, some
explicitly identified as such and others implicitly operating as such. In
this section, we identify a collection of problems that are likely to shape
HRI in the near future. For each problem, we discuss those aspects of
the problem that make it particularly challenging and useful.
USAR is the most high profile of the HRI challenge problems. The
attributes that define this as a challenge problem include the highly
unstructured nature of USAR environments. This imposes strict chal-
lenges on robot mobility, communications, map-building, and situation
Developing robots to be used in military reconnaissance and com-
bat is another high profile challenge area in HRI. Similar to USAR,
environments tend to be unstructured, but perhaps more importantly
operators may be required to operate under extreme stress in the pres-
ence of an adversary that is trying to prevent their success.
Space robotics is another area where the environment is often
unstructured, and the environment is often extreme in terms of tem-
perature, radiation, the vacuum of space, and the presence of dust.
Important characteristics of space robotics include the observation
that operators can be highly trained, but communications may be
very limited due to time delays, power limitations, and even opera-
tor mobility (as in the case of an astronaut interacting from within a
space suit).
Assistive robotics is a challenge area, but not because the environ-
ment is unstructured per se. Rather, the key attributes of this problem
6.3 Solution Themes 245
are the proximity and vulnerability of the human in the interaction
plus the potential for interactions that may evolve in unanticipated
Humanoid robotics is a challenge area, both in terms of engineer-
ing human-like movements and expressions, and in terms of the chal-
lenges that arise when a robot takes a human form. With such a form,
social and emotional aspects of interaction become paramount.
Natural language interaction is a challenge problem, not only
because it requires sophisticated speech recognition and language
understanding, but also because it inevitably includes issues of mixed-
initiative interaction, multi-modal interaction, and cognitive modeling.
6.3 Solution Themes
HRI presents a number of problems that cross application domains.
These problems include requirements on autonomy, information shar-
ing, and evaluation. Emerging from these problems are a set of solu-
tion themes that cross applications and that, when addressed, can be
leveraged across several problems. In this section, we identify some of
these solution themes and discuss some of the open questions associated
with them.
Dynamic Autonomy, Mixed-Initiative Interaction, and Dialog.
Because most interesting applications of human–robot interaction
include rich information exchanges in dynamic and complex environ-
ments, it is imperative that interactions and resulting behaviors can
accommodate complexity.
Telepresence and Information Fusion in Remote Interaction.
Although remote control and teleoperation are the oldest forms of
human–robot interaction, the problem is far from solved. In fact, with
advances in robot morphology, sensor processing, and communications,
it is necessary to find new ways to fuse information to provide humans
an operational presence with the robot. Obstacles to achieving this
include bandwidth limitations, communications delays and drop-outs,
mismatches in frames of reference, communicating intent and trusting
autonomy, and mismatches between expectations and behaviors.
246 Solution Themes, Scientific Approaches, and Challenge Problems
Cognitive Modeling. Effective interactions between humans include
a common ground created by common experiences and cultures. This
common ground creates realistic expectations and forms the basis com-
munications. From a robot’s perspective, supporting effective interac-
tions also requires establishing and maintaining common ground. An
emerging approach to doing this is to create cognitive models of human
reasoning and behavior selection. The goal is to create rich enough mod-
els either (a) to allow the robot to identify a human’s cognitive state
and adjust information exchange accordingly or (b) to allow the robot’s
behavior to be generated by models that are interpretable by a human.
Team Organizations and Dynamics. Many HRI researchers are
striving to develop systems that allow multiple robots and multiple
humans to interact with each other. To accomplish this, it is necessary
to shape team interactions and dynamics by establishing organizational
structures, communications protocols, and support tools. Team organi-
zations necessarily subsume different and dynamic roles, which implies
that such efforts will need to leverage lessons from research on mixed
initiative and dialog.
Interactive Learning. Because the world is complex, interactions
between humans and robots are also complex. This implies that it
is impossible to anticipate every conceivable problem and generate
scripted responses, or anticipate every conceivable percept and gen-
erate sensor processing algorithms. Interactive learning is the process
by which a robot and a human work together to incrementally improve
perceptual ability, autonomy, and interaction.
Relation to Other Fields
Although we have framed HRI as a new field in this review, HRI has
strong ties to previous and ongoing work in telerobotics, intelligent
vehicle systems, human–computer interaction, etc. In this section, we
review many of the stronger ties to these fields. We begin with the most
relevant: telerobotics and supervisory control.
7.1 Telerobotics and Teleoperation
Sheridan’s papers and books on telerobotics and supervisory control
are perhaps the most influential in the field. In 1992, his book outlined
the state of the art in human–robot interaction, with an emphasis on
open problems, mathematical models, and information flow [267]. This
book was followed by a 2002 updated survey and framework of human
factors for the general human–machine interaction problem [268].
Even more influential than his books, perhaps, is Sheridan’s and
Verplank’s levels of automation in human–machine interaction. These
10 levels of automation span the range from direct control through deci-
sion support to supervisory control [269]. More recently, Parasuraman
and Wickens teamed with Sheridan to extend these 10 levels of automa-
tion beyond decision support to other aspects of human–machine
248 Relation to Other Fields
interaction [248]. Levels of automation foreshadow more recent con-
cepts of dynamic autonomy in all its forms [67, 144].
In addition to these seminal works, there are numerous examples
of remote robot operation. One example comes from attempts during
World War II to remotely control aircraft. This lead to the study of
remotely piloted vehicles [95], a precursor of more modern work on the
Human Factors of Unmanned/Uninhabited Aerial Vehicles [322].
Complementing the work on remotely piloted aircraft is a work on
unmanned underwater vehicles (UUVs). This work includes both mil-
itary and scientific applications, and spans topics of remote visualiza-
tion, telepresence, and information display [329].
7.2 Human Factors and Automation Science
The field of human factors emerged as the confluence of engineering
psychology, ergonomics, and accident analysis. Human factors work
relevant to HRI includes important lessons from thought provoking
papers such as Bainbridge’s “Ironies of Automation” [14] and Hancock’s
position paper on the make-up of HRI teams [121]. Human factors work
is motivated by numerous stories, sometimes humorous and sometimes
sobering, from years of humans interacting with automation in various
forms [50].
The human factors literature has produced key concepts of inter-
action, such as mental workload [110, 204], situation awareness [88],
mental models [142, 300], and trust in automation [173]. It also includes
several themes, frameworks, and models that provide a solid founda-
tion for describing and predicting responses to human–robot interac-
tion. These contributions include the seminal work of Rasmussen who
presented a hierarchy of interaction that included knowledge-based,
rule-based, and skill-based interactions [237]. Rasmussen’s hierarchy
is a human factors complement to hierarchical and intelligent control
[7, 183, 255]. Contributions also include general principles of cognitive
ergonomics, with particularly powerful ideas such as Wickens’s Multi-
ple Resource Theory [314]. Complementing these models are interac-
tion phenomena that are common enough that they are elevated to the
status of law by David Woods [320].
7.3 Aviation and Air Traffic Control 249
Rich as these models and laws are, they cannot substitute for prac-
tical real-world observation. This point was strongly made by Hutchins
in the book “Cognition in the Wild” [130]. In the spirit of real-world
observation, the field of ethnography has developed a set of methodolo-
gies for recording observations in real-world settings, and some ethno-
graphers have tried to translate these observation and summarization
methods into tools for designing interventions [57, 58, 93].
Growing out of the need to understand the goals, tasks, and informa-
tion flow of existing processes, a series of methodologies have emerged
that produce formalized models for how “things get done.” These
methodologies include Goal-Directed Task Analysis, Cognitive Task
Analysis, and Cognitive Work Analysis [88, 301]. These methodologies
produce models of goals, tasks, and information flow, which are being
used in HRI [4]. Complementing these high-level models are cognitive
models of the mental processes used to accomplish tasks, and activity
analyses of existing work practices. The cognitive models and activity
analyses are especially interesting to HRI because they can be used
not only as models of existing processes, but also as tools to generate
behaviors such as perspective-taking and planning [295].
It is worth noting that cognitive psychology and social psychology
offer perspectives and insights that are distinct from traditional human
factors. There is a trend in HRI to include cognitive and social scien-
tists in collaborative research efforts with roboticists, human factors
engineers, and experts in human–computer interaction.
Given the rich history of human factors and the recent emergence of
HRI, it is unfortunate and perhaps inevitable that some relevant human
factors work is called by different names in the different fields. Exam-
ples include adjustable autonomy and Inagaki’s Situation-Adaptive
Autonomy [132, 133]; and augmented reality/virtuality and synthetic
vision [48].
7.3 Aviation and Air Traffic Control
Modern aircraft are among the most capable semi-autonomous systems
in use. Moreover, because of the safety critical nature of aviation, air-
craft systems must be extremely robust and reliable. Careful human
250 Relation to Other Fields
factors analyses are often performed to justify a change to an aircraft
system. From one perspective, an aircraft is a very capable type of
robot, albeit one that happens to carry the human operator.
As a result, HRI has many lessons that it can learn from aviation,
both in terms of useful technologies and careful human factors analysis.
Relevant examples include ground proximity warning systems, which
use multi-modal communications coupled with robust autonomy to pre-
vent controlled flight into terrain [63]. Tunnel-in-the-sky displays can
increase situation awareness by helping pilots to understand how con-
trol choices will affect the trajectory of the aircraft [195]. Problems
caused by mode confusion, by the operator being out of the loop, by vig-
ilance, by excessive workload, and by team coordination issues have all
received attention and been mitigated by procedures and technologies.
As robots become more capable, an important issue is how many
robots can be managed by a single human. This question makes another
aspect of aviation relevant to HRI, namely, human factors work done
with the air traffic control problem (ATC). ATC is a problem that
involves sequencing, deconflicting, and handing off multiple highly
capable systems [123]. Indeed, the autonomy level of these aircraft
is extremely high, since they consist of both a trained and intelli-
gent human operator as well as aircraft autonomy. Nevertheless, ATC
imposes high workloads on operators. Careful human factors analyses
have been performed and mitigating technologies have been developed
[86, 214]. Because of the safety critical nature of ATC, many potentially
useful technologies have not been incorporated into ATC systems. Even
so, some ATC-related research and development could serve as a type
for HRI problems.
There are three other aspects of aviation and ATC that are very rele-
vant to HRI. First, ATC training and certification programs have many
desirable attributes that could be imitated in HRI. Second, because
aviation incidents are relatively rare and, when they occur, can dam-
age career prospects, the aviation industry has developed anonymous
reporting procedures which are kept in a database of problems that
have occurred. As HRI matures, it could be useful to create a standard-
ized reporting system to identify and mitigate problems that frequently
arise. Third, the aviation industry has a strong set of standards. There
7.4 Intelligent Vehicle Systems 251
have been recent efforts to bring the standardization process to HRI
[92], though it is important that these efforts do not impose undue
restrictions on creativity and design.
7.4 Intelligent Vehicle Systems
The field of intelligent vehicle systems (IVS) has received considerable
attention in recent decades, including the emergence of several confer-
ences and journals [1, 2, 3, 288]. The field of intelligent vehicle systems
has many problems in common with HRI, including designing auton-
omy that supports human behavior, creating attention-management
aides, supporting planning and navigation under high-workload con-
ditions, mitigating errors, and creating useful models and metrics
[27, 108, 109]. Indeed, a strong case can be made that modern automo-
biles are just semi-autonomous robots that carry people.
IVS not only include automobiles, but also trains, busses, semi-
trucks and other forms of public transit [25]. The users of IVS range
from those that are highly trained to those that are untrained and
sometimes even uninformed. Moreover, IVS must be designed to be
safety-critical, time-critical, and to operate under high workload con-
ditions. The presence of untrained operators and high-demand tasks
produces technologies that may be relevant for those aspects of HRI
that require interaction with bystanders or na¨ıve operators.
7.5 Human–Computer Interaction (HCI)
As the field of HRI has grown, it has seen many contributions from
researchers in HCI and it has been nurtured by HCI organizations. For
example, the first International Conference on Human–Robot Inter-
action was sponsored by ACM’s Computer–Human Interaction Special
Interest Group [265]. HRI research is attractive to many members of the
HCI community because of the unique challenges posed by the field.
Of particular interest is the fact that robots occupy physical space.
This offers unique challenges not offered in desktop metaphors or even
pervasive computing. Physical location in a 3D space imposes strong
requirements on how information is displayed in remote operation, and
252 Relation to Other Fields
even stronger requirements on how space is shared when robots and
humans occupy the same space. HRI benefits from contributions from
HCI researchers, both in methodologies, design principles, and com-
puting metaphors.
7.6 Artificial Intelligence and Cybernetics
Because of their emphasis on designing intelligence for human-built
systems, the fields of artificial intelligence (AI) and cybernetics have a
great deal of relevance to the field of HRI. Intelligence and autonomy
are closely aligned. Indeed, when experimenters want to give the illusion
of truly intelligent robots, it is common to use a “wizard of Oz” design
wherein experiment participants believe that they are controlling an
intelligent robot but where in reality the commands that they issue
are received and translated into teleoperation commands by a hidden
human [116].
HRI frequently uses concepts from AI in the design of autonomy
algorithms. Moreover, AI techniques have informed and been informed
by concepts from cognitive science. For example, the ACT-R system,
a popular tool for modeling cognition, uses AI-like production rules.
Such cognitive models have increasingly become relevant to HRI, both
as tools for modeling how a human might interact and as the basis for
generating robot behavior [295].
Although sometimes justifiably treated as a separate field from AI,
augmented reality and telepresence have much relevance to HRI. Aug-
mented reality techniques are used to support remote interactions in
NASA’s Robonaut [9]. Augmented virtuality and mixed reality are vari-
ations of augmented reality that have found application in HRI [208].
Some suggest that telepresence, the natural extension of human aware-
ness of a remote space, is a goal of interface design in HRI, though
others note that a feeling of remote presence is not necessary provided
that information is displayed in a way that supports intentional action
in the remote space [208].
Another AI-related area that has developed into a separate field
of study is computer vision. Computer vision algorithms are frequently
used to translate camera imagery into percepts that support autonomy.
7.7 Haptics and Telemanipulation 253
Moreover, these algorithms are also used to provide enhanced aware-
ness of information through the use of image stabilization, mosaics,
automated target recognition, and image enhancement.
Many AI techniques are used in computer games. These games,
some of which are very sophisticated, provide a probe into the levels of
autonomy needed to support useful interactions. Given these levels of
autonomy, information is integrated and presented to operators in sev-
eral different forms; evaluating these forms of information presentation
provides guidelines for interface designers in HRI [241]. Sophisticated
multi-player online games may become useful in understanding how
natural language can be used to support HRI and how human–robot
teams should interact.
Finally, machine learning is a subfield of AI that is proving very
useful in robotics and HRI. Machine learning can be used to develop
robot behaviors, robot perception, and multi-robot interaction [40, 89,
207]. Interactive learning has received attention as a way to capture
and encode useful robot behaviors, to provide robot training, and to
improve perception. Interactive techniques with intelligent systems are
also present in AI. Interactive proof system, interactive planners, and
“programming by reward” in machine learning are all examples of how
human input can be used in collaboration with AI algorithms.
7.7 Haptics and Telemanipulation
Before concluding the review, it is important to note that much of
the field of haptics and telemanipulation are aligned with the goals
and challenge problems of HRI. However, the current research culture
tends to treat haptics/telemanipulation separate from HRI, perhaps
because of the longer history of the field of haptics. Since the two fields
have much to learn from each other, it is desirable that the research
communities increase interactions.
Human–robot interaction is a growing field of research and application.
The field includes many challenging problems and has the potential
to produce solutions with positive social impact. Its interdisciplinary
nature requires that researchers in the field understand their research
within a broader context. In this survey, we have tried to present a
unified treatment of HRI-related problems, identify key themes, and
discuss challenge problems that are likely to shape the field in the near
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... Since improving organizational performance is the ultimate goal, organizations will not apply AI technology regardless of costs and risks (Lu and Chen, 1994). It is a feasible, efficient, and cost-saving way to automate part of the work of organizations and realize human-AI collaboration (HAC), especially in knowledge-intensive work (Goodrich andSchultz, 2007, Davenport andKirby, 2016). HAC has become a significant research field (Makarius et al., 2020, Østerlund et al., 2021, Seeber et al., 2020, Rai, Constantinides, and Sarker, 2019, and excellent cooperation between the two sides is a critical prerequisite for the improvement of efficiency and performance of organizational integrated AI. ...
... Not good at leading and managing teams. Lack of human flexibility in teamwork (Ho et al., 2021, Goodrich andSchultz, 2007). Lack of communication skills (Cheng et al., 2022, Davenport andKirby, 2016). ...
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Artificial Intelligence (AI) can enter organizations and become AI teammates in organizational teams, posing new challenges to organizational form, team management, and team working patterns. Establishing an efficient "human-AI team" requires an organization and its members to have superior digital capabilities and develop effective capability improvement strategies to realize the organizational socialization process of AI. In this paper, we adopt the Design Research (DR) approach in building a Human-AI Collaboration Platform (HACP) in a work team, integrating AI teammates based on HACP, and optimizing the platform and human-AI collaboration. Considering organizational digital capabilities as one of the core components, we propose a three-level framework of digitalization capabilities for individuals, organizations, and across organizations, as well as the enhancement strategies of such capabilities. Besides, we clarify the foundation of digitalization capabilities and design, implement, and verify the enhancement strategies. The construction and successful implementation of the HACP in our study verify the effectiveness of the proposed capability framework and provide principle guidance for organizations to implement effective strategies to improve digital capabilities to facilitate the organizational socialization of AI teammates.
... Moreover, persons with clinically encumbered visual and auditory senses can benefit from haptic interventions in an otherwise visual-and auditory-centric world, including approximately 1.1 billion people worldwide living with a loss of vision (of whom 345 million have moderate to severe impairment or complete blindness) and another 1.5 billion people with a loss of hearing (of whom 430 million have moderate to severe cases). 1,2 As a consequence, haptic technologies serve as an integral tool for both supplementing and complementing clinically impeded or otherwise saturated modes of conventional communication, especially regarding the navigation of ''noisy'' environments, [3][4][5] immersion into augmented or virtual realities, [6][7][8][9][10][11] human-robot interaction, [12][13][14][15] and general assistance throughout our activities of daily living. [16][17][18][19] Haptic stimuli include sensations relating to temperature (such as hot or cold cues), [20][21][22][23] pain, and mechanoreception (i.e., forces or pressures applied to the skin through mechanical, electrical, 24 or ultrasonic means 25 ); tactile mechanoreceptors in particular represent a proven route for interpreting spatially and temporally distinct cues. ...
... Further experiments using this instrumented wrist rig and the details thereof can be found in prior works by the co-authors. [10][11][12][13][14][15] ...
... Robot is an umbrella term that describes a miscellaneous collection of (semi-)automated devices with various capabilities, technologies, and appearances [53]. These cyber-physical systems are often differentiated by their Degrees-of-Freedom (DoF) or ability to move and manipulate their environment. ...
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Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.
... The field has received an enormous amount of attention in the last few years but its foundational concepts were articulated in the literature decades before the technical constraints were lifted and robots of sufficient complexity were developed. Isaac Asimov's three laws of robotics are often cited as the original guidelines for HRI, but there are other historical examples as well [1,2]. ...
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This retrospective study presents and summarizes our long-term efforts in the popularization of robotics, engineering, and artificial intelligence (STEM) using the NAO humanoid robot. By a conservative estimate, over a span of 8 years, we engaged at least a couple of thousand participants: approximately 70% were preschool children, 15% were elementary school students, and 15% were teenagers and adults. We describe several robot applications that were developed specifically for this task and assess their qualitative performance outside a controlled research setting, catering to various demographics, including those with special needs (ASD, ADHD). Five groups of applications are presented: (1) motor development activities and games, (2) children’s games, (3) theatrical performances, (4) artificial intelligence applications, and (5) data harvesting applications. Different cases of human–robot interactions are considered and evaluated according to our experience, and we discuss their weak points and potential improvements. We examine the response of the audience when confronted with a humanoid robot featuring intelligent behavior, such as conversational intelligence and emotion recognition. We consider the importance of the robot’s physical appearance, the emotional dynamics of human–robot engagement across age groups, the relevance of non-verbal cues, and analyze drawings crafted by preschool children both before and after their interaction with the NAO robot.
... Например, такие устройства применяют для обучения [Алексеева, 2020], рекреации и вовлечения в игровую деятельность детейдошкольников [Шандаров и др., 2014], в терапии расстройств аутистического спектра [Петрова, 2017], для ухода за пожилыми людьми и в домах престарелых [Wada, Shibata, 2007;van Maris et al., 2020]. В этих исследованиях роботы выступают как интеллектуальные системы разной степени автономности, что уже давно фиксируется в таком направлении исследований, как взаимодействие людей и роботов (Human-Robot Interaction) [Sheridan, 2016;Goodrich, Schultz, 2007] ...
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В статье предпринята попытка реконструировать категорию агентности применительно к автономным интеллектуальным системам в контексте человекомашинного взаимодействия с социальными роботами. Представлен обзор эмпирических исследований взаимодействия людей и социальных роботов, диапазон которых распространяется от простых дескрипций до изучения доверия и реципрокности. Показано, что технические разработки в сфере социальной робототехники и реальный, эмпирически фиксируемый способ интеракций людей и социальных роботехнических систем может быть довольно консистентно интерпретирован через теоретическую призму объектно-ориентированных онтологий и акторно-сетевой парадигмы.
Healthcare workspaces would greatly benefit from the employment of robotic assistants in both clinical and non-clinical tasks. However, despite their advantages, a major shortcoming for the deployment of robots limiting their widespread acceptance by the market is the fact that existing robotic solutions were originally designed for large industrial and warehouse spaces. These are characterized by structured spaces and predictable environments, where robots move along predefined paths and interaction with humans is typically not required. Herein, we examine state-of-the-art computer vision methods that enable robots to detect the presence and identify the type of dynamic obstacles inside their visual field and adapt their navigation accordingly. To achieve this goal, we trained our robots using contemporary deep learning methods (namely YOLO-You Only Look Once architecture and its variations) and obtained promising results in both human and robot detection. For that purpose, a newly constructed dataset consisting of robot images was used, complementing the well-known COCO dataset. Overall, the present study contributes towards the key objective of safe robot navigation in healthcare spaces and underpins the wider application of studies on Human-Robot Interaction in less structured environments.
Today’s retail store is a hotbed of constant improvement and innovation to provide a seamlessly smooth experience to the customer. With the permeating use of AI/ML and IoT technology, retail chains are moving rapidly to deploy such innovations to their retail stores to improve physical process efficiency and make the customer experience as smooth as possible. Major retailers like Walmart and Ahold Delhaize are experimenting with using robots to automate repetitive tasks like shelf replenishment. There is little research on the issue of automating the replenishment of merchandise inside retail stores during periods of heavy demand for a particular product. In such cases, employees are usually seen scrambling to refill shelves during store operations, thus interfering with customer experience and decreasing physical process efficiency in general. Our goal is to study an approach to automate the process of replenishing products dynamically by leveraging real time data using shelf-stocking robots. Specifically, we study algorithms that dynamically route shelf-stocking robots while minimizing interference with shoppers. The routing algorithms proposed would incorporate congestion awareness to respond to shopper dynamics, thus avoiding interference with shoppers. One key challenge is to model shopper behavior which can be expected to influence the performance of such algorithms. This exploratory research has the potential to influence the way store fronts of the future are designed, enabled by a new class of algorithms for material handling that improves service levels in nextgen storefronts.
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This systematic literature review (SLR) explored the uncanny valley (UV) effect as related to human-robot interaction (HRI). The purpose of this SLR was to investigate the variables of the UV effect to find recommendations from the results that would reduce occurrence of the phenomenon. Using a textual narrative synthesis approach, a six-step methodology was used to answer three research questions: 1.) What HRI variables predict the UV effect? 2.) When considering HRI, what other variables are related to the UV effect? 3.) What recommendations can be made that would potentially reduce the UV effect that occurs during HRI? Anthropomorphism, human-likeness, mind perception, agency, experience, combination and mismatch variables, and movement emerged as variables that predict the UV effect. Other variables discovered to be related to the UV effect included human age, robot identity, and disclosure and transparency. Recommendations for reducing the UV effect include revealing a robot’s identity, aligning robot identity, function, and user, and designing robots that look like cartoons and caricatures. Recommendations for future HRI-UV research are discussed as well.
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Source search is an important problem in our society, relating to finding fire sources, gas sources, or signal sources. Particularly, in an unexplored and potentially dangerous environment, an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem. Such environments could be completely unknown and highly complex. Therefore, novel search algorithms have been designed, combining heuristic methods and intelligent optimization, to tackle search problems in large and complex search spaces. However , these intelligent search algorithms were not designed to address completeness and optimality, and therefore commonly suffer from the problems such as local optimums or endless loops. Recent studies have used crowd-powered systems to address the complex problems that machines cannot solve on their own. While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable, whether using the power of the crowd can improve autonomous source search algorithms remains unanswered. To this end, we propose a crowd-powered source search approach enabling human-AI collaboration, which uses human intelligence as external supports to improve existing search algorithms, and meanwhile reduces human efforts using AI predictions. Furthermore, we designed a crowd-powered prototype system, and carried out an experiment with both experts and non-experts, to complete 200 source search scenarios (704 crowdsourcing tasks). Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency. Our work provides valuable insights in human-AI collaborative system design.
Service scholars seem to have empirically overlooked the impact of service robots in the overall customer evaluation of tourism services. This study addresses this gap by leveraging three-factor theory and electronic Word-Of-Mouth data to assess human-robot interaction’ influence on customer satisfaction. Text analytics are deployed alongside a penalty-reward contrast technique on almost 70,000 online reviews spanning 44 hotels worldwide that incorporated service robots into their operations. Customer satisfaction with hospitality services is significantly increased by positive service robots’ performance, while no significant effect is associated with negative service robots’ performance. The traveler type does not moderate the relationship between service robots’ performance and customer satisfaction. These findings, confirmed through Propensity Score Matching, reveal that service robots constitute an “excitement factor” in hospitality service offerings, thus providing a strong incentive for their integration into the workforce. Policymakers are urged to proactively facilitate the transition to a more automated service economy.
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Bioinspired technology breakthroughs of insect inspired navigation are enabling small aircraft that will allow exploration of Mars. The bioinspired technologies under development consist of a bioinspired navigation-control system, human vision inspired and birds of prey inspired search and track systems. Two classes of new missions for Mars exploration: 1) the long range exploration missions and 2) critical ephemeral phenomena observation missions can be enabled by incorporation of these bioinspired technology breakthroughs in such flyers. In this paper we describe our implementation of an image based guidance algorithms that can be used for imaging the descent and landing of mission payloads on Mars, thus potentially enabling a "black box" capability as a unique risk mitigation tool for landed missions of all types. Further on, such capabilities could enable observation of a variety of other ephemeral phenomena such as tracking dust storms in real-time. Copyright © 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Intelligent Machines, like Intelligent Robots are capable of performing autonomously in uncertain environments, and have imposed new design requirements for modern engineers. New concepts, drawn from areas like Artificial Intelligence. Operations Research and Control Theory, are required in order to implement anthropomorphic tasks with minimum intervention of an operator. This work deals with the analytic formulation the Principle of Increasing Precision with Decreasing Intelligence the fundamental principle of Hierarchically Intelligent Control. A three level structure representing the Organization. Coordination and Execution has been developed as a probabilistic model of such a system and the approaches necessary to implement each one of them on an intelligent machine are discussed. The Principle is derived also from a probabilistic model and can be expressed in terms of entropies. It is compatible with the current formulation of the Hierarchically Intelligent Control problem, the mathematical programming solution of which minimizes the total Entropy. The derivation and design of parallel architectures for Artificial Intelligence, like the Boltzmann machine is obtained from such formulation.
This paper discusses the ways in which automation of industrial processes may expand rather than eliminate problems with the human operator. Some comments will be made on methods of alleviating these problems within the 'classic' approach of leaving the operator with responsibility for abnormal conditions, and on the potential for continued use of the human operator for on-line decision-making within human-computer collaboration.
Dante II is a unique walking robot that provides important insight into high-mobility robotic locomotion and remote robotic exploration. In 1994 it was deployed and successfully tested in a remote Alaskan volcano. For more than five days the robot explored alone in the volcano crater using a combination of supervised autonomous control and teleoperated control. The robot and field experiment are first overviewed to provide context for the focus of the paper—lessons learned. It is the degree by which we can learn from the Dante project that will determine its lasting significance.
Today, approximately 10 percent of the world's population is over the age of 60; by 2050 this proportion will have more than doubled. Moreover, the greatest rate of increase is amongst the "oldest old," people aged 85 and over. While many older adults remain healthy and productive, overall this segment of the population is subject to physical and cognitive impairment at higher rates than younger people. This article surveys new technologies that incorporate artificial intelligence techniques to support older adults and help them cope with the changes of aging, in particular with cognitive decline.
Using current research and discussion of the topic along with clear applications, Modern Experimental Design highlights the guiding role of statistical principles in experimental design construction. This text can serve as both an applied introduction as well as a concise review of the essential types of experimental designs and their applications. Topical coverage includes designs containing one or multiple factors, designs with at least one blocking factor, split-unit designs and their variations as well as supersaturated and Plackett-Burman designs. In addition, the text contains extensive treatment of: Conditional effects analysis as a proposed general method of analysis Multiresponse optimization Space-filling designs, including Latin hypercube and uniform designs Restricted regions of operability and debarred observations Analysis of Means (ANOM) used to analyze data from various types of designs The application of available software, including Design-Expert, JMP, and MINITAB This text provides thorough coverage of the topic while also introducing the reader to new approaches. Using a large number of references with detailed analyses of datasets, Modern Experimental Design works as a well-rounded learning tool for beginners as well as a valuable resource for practitioners.