# Communicative Robot Signals: Presenting a New Typology for Human-Robot
Interaction

Patrick Holthaus<EMAIL_ADDRESS>0000-0001-8450-9362 University of
HertfordshireSchool of Physics, Engineering and Computer ScienceCollege
LaneHatfieldUnited KingdomAL10 9AB ,  Trenton Schulz<EMAIL_ADDRESS>0000-0001-6217-758X Norwegian Computing CenterDepartment of Applied Research
in ICTOsloNorway ,  Gabriella Lakatos<EMAIL_ADDRESS>0000-0003-1436-7324 University of HertfordshireSchool of Physics, Engineering
and Computer ScienceCollege LaneHatfieldUnited KingdomAL10 9AB and  Rebekka
Soma<EMAIL_ADDRESS>0000-0002-1074-0366 University of OsloDepartment of
InformaticsOsloNorway

(2023)

###### Abstract.

We present a new typology for classifying signals from robots when they
communicate with humans. For inspiration, we use ethology, the study of animal
behaviour and previous efforts from literature as guides in defining the
typology. The typology is based on communicative signals that consist of five
properties: the origin where the signal comes from, the deliberateness of the
signal, the signal’s reference, the genuineness of the signal, and its clarity
(i.e., how implicit or explicit it is). Using the accompanying worksheet, the
typology is straightforward to use to examine communicative signals from
previous human-robot interactions and provides guidance for designers to use
the typology when designing new robot behaviours.

human-robot interaction, typology, model, communicative signals

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 ACM/IEEE International Conference on Human-Robot Interaction; March
13–16, 2023; Stockholm, Sweden††booktitle: Proceedings of the 2023 ACM/IEEE
International Conference on Human-Robot Interaction (HRI ’23), March 13–16,
2023, Stockholm, Sweden††doi: 10.1145/3568162.3578631††isbn:
978-1-4503-9964-7/23/03††ccs: Human-centered computing Interaction
paradigms††ccs: Human-centered computing Interaction design theory, concepts
and paradigms††ccs: Human-centered computing Interaction design process and
methods

##  1\. Introduction and motivation

We have all struggled with making ourselves understood. Communicating in a
clear and unambiguous way is a difficult task regardless if the parties
involved are human, animal, or robot. Modelling communicative behaviour can
aid in implementing communication between humans and robots. We argue that
designing communication in human-robot interaction (HRI) can benefit from a
more structured approach to modelling communicative signals since the robot’s
interactive capabilities are human-made. Such a model or typology can offer
consistent and accessible ways to design robot interactions and identify and
describe communicated content. It thereby helps to address the following
problems:  (a) designing robot behaviours that communicate what was intended
and  (b) examining (mis-)communication in HRI (and experiments).  There have
been attempts at creating typologies in the past. Some have borrowed ideas
from animal communication (Hegel et al., 2011). Others have based themselves
on concepts such as speech acts and intentional and consequential sounds
(Schulz et al., 2021). Others have used psychological inferences and notation
from dance to analyze and interpret behaviours (Bianchini et al., 2016).
Still, none of these attempts are completely satisfying; for example, there is
uncertainty around interaction that is implicit by design or implicit by
chance.

In this article, we present a new typology to make designing and classifying
robot behaviours more straightforward. The theoretical frame for this new
typology is rooted in communication theories with a focus on ethology, the
field of animal behaviour, and draws inspiration from earlier approaches in
characterising communication between humans and robots. The accompanying
worksheet for the typology aims to provide roboticists with a better overview
and understanding of robot communicative signals by taking a communicative
perspective on robot behaviours, addressing one of the central challenges in
social robotics (Sheridan, 1997). We begin by looking at ethology and how an
ethological approach can be useful in HRI (Sect. 2). We then discuss how
ethological and other communication theories are currently being applied in
HRI contexts (Sect. 3). This leads to the presentation of the new typology
with examples, illustrating how it can be used in robot behaviour design or
applied to existing studies (Sect. 4). We briefly discuss how the typology can
complement other approaches and some current limitations with the typology
(Sect. 5) before we conclude the paper (Sect. 6).

##  2\. An ethological approach

Communication between humans and robots can be understood by looking at it
from an evolutionary perspective (Maynard Smith and Harper, 2003; Laidre and
Johnstone, 2013). Ethology helps us to find a feasible way of explaining and
examining the quality of signals by  (a) looking at how communicative signals
evolved and are used in the animal kingdom, and  (b) by looking at asymmetric
communication between humans and animals and drawing parallels to asymmetric
communication between humans and robots  . Let us examine concepts and
theories commonly employed in ethology and see how they can be useful for HRI
and thus for the typology of communicative robot signals we introduce in Sect.
4.

###  2.1. Communication definitions

Communication in ethology is defined as the behavioural act an animal performs
to change the probability another animal will behave in a certain way (Slater,
1985). Often, this act has adaptive value, at least for the sender animal. In
addition, signals should have an evolutionary history and should be evolved
specifically for a communicative function.

Communication, in general, is the process or act of transmitting a message
from a sender to a receiver through a channel with the interference of noise
(DeVito, 1986). Some broaden the definition to include that the message
transmission is intentional and conveys meaning to bring about change. Others
restrict the definition to  (a) the receiver must respond to the message and
(b) the interacting individuals must be members of the same species
(Goodenough et al., 2009).  A communicational act includes at least two
individuals, a sender and a receiver. The sender emits a signal that informs
the receiver about the sender’s inner state or about elements of the
environment. The signal’s function in the receiver’s mental model can change
by ontogenetic learning and, thus, one signal can have different functions in
different contexts. In this sense, the message of a signal is the function the
signal fills in the receiver’s mental model.

Csányi and Kampis split communication based on how well the mental models of
the participants correspond (Csányi and Kampis, 1988). When a component of the
sender’s mental model (the signal) has the same function in the receiver’s
mental model, the correspondence between the two mental models is 100%. This
is type I communication. This type of communication rarely occurs, even among
individuals of the same species. In type II communication, the correspondence
is below 100% and cannot be exactly determined. In the inter-species
(interspecific) situation, one can only talk about type II communication. The
main challenge of such is that the sender’s and receiver’s mental models
require a common set of signals to communicate.

Based on the signals’ nature, researchers differentiate between referential
and non-referential (motivational) signals (Kampis, 1991). Non-referential
signals contain information about the actual motivational state of the animals
and are independent of the quality of any external stimuli. Referential
signals refer to certain elements of the environment or environmental events
independent of the inner state of the sender. Referential communication
assumes the signals referring to the elements of the environment have the same
role as words in human language, but theoretical and experimental research has
shown that function and mechanism cannot be separated since the inner state of
the animals cannot be determined. For this reason, researchers developed
functional reference (Evans et al., 1993; Macedonia and Evans, 1993; Evans,
1997). This concept implies that communicational signals have the same
function in the animals’ communicational system as words in the human
language, but it is not assumed that there are similar mechanisms behind the
signal production and signal utilisation (Evans, 1997). Some of these
definitions reference interspecific communication, which is relatively rare
among animals. The function of communication (both in intra- and interspecific
cases) can be to share information or manipulate others. The most commonly
investigated examples of interspecific communication are alarm calls, which
are classic examples of functionally referential signals. For example, Vervet
monkeys (Cercopithecus aethiops) can comprehend some signals of a starling
species (Spreo superbus) living close to them (Cheney and Seyfarth, 1990). The
starling produces specific alarm calls for different predators (terrestrial or
air). Vervet monkeys show adequate reactions to a call, avoiding the type of
predator that it refers to. Hence, it can be supposed that the monkeys can
comprehend the meaning of these calls. It is, however, debatable if such
communication is completely bi-directional or simply eavesdropping from the
receiver’s side.

An extraordinary case of interspecific communication is when a communicational
event develops between an animal species and humans. Greater honeyguides
(Indicator indicator) living in East-Africa have assisted humans living close
to them in finding honey for about 20,000 years, guiding them to the hives of
wild bees (Apis mellifera), which are well-protected and out of the
honeyguide’s reach. The bird’s vocal signals and the height of their flight
predict the distance to the bees’ nest. In addition, communication between the
honeyguide and humans is not unidirectional; members of the Boran tribe also
call the honeyguide using specific vocal signals (Isack and Reyer, 1989) using
a dedicated whistle. The honeyguide responds by flying towards the campsite of
the Boran, calls for the human’s attention and then guides them to the hive.
The bird uses specific vocal signals and movements, knowing that when the
Borans obtain the honey, they always leave some honey behind for the
honeyguide.

###  2.2. How can ethology help robotics?

The etho-robotic approach suggests that HRI should be considered a specific
form of inter-specific interaction. Applying animal models in robot behaviour
design thus provides an important alternative in the development of social
robots (Miklósi and Gácsi, 2012). Human-animal interaction is a valid model
for HRI as both are asymmetric, may start at any age, are much simpler than
human-human interactions, and can develop using only nonverbal communicational
behaviour on the animal (or robot) side (Lakatos et al., 2014). Hence, much
like in type II communication between animals, a common set of communicative
signals is required for successful interaction between humans and robots.

Dogs provide an excellent biological model since they adapted to the human
social environment exceptionally well, developing specific interspecific
communicational skills towards humans, enabling them to participate in
numerous complex social interactions with us on a daily basis (Topál et al.,
2009). Dogs have not only evolved exceptional interspecific communicational
skills but also develop a strong attachment bond with humans, making them
life-long companions (Topál et al., 1998). This approach argues that the
implementation of dog-analogue behaviours in social robots could lead to more
believable and acceptable robotic companions (Miklósi and Gácsi, 2012; Miklósi
et al., 2017; Vincze et al., 2012).

##  3\. Communication Theories and HRI

Let us examine some HRI studies that have used behaviour drawn from animals,
humans, and machines for specific interactions. Then, we present how general
theory and the arts can also be used for designing communication. We also
present some earlier attempts at characterising communicative robot signals in
general.

###  3.1. Studies inspired by etho-robotics, human, and artificial behaviour

There are several experimental studies that have used an etho-robotics
approach, and several have been inspired by behaviours from dogs. One study
used dogs’ motivational (non-referential) signals as a model to design
emotionally expressive behaviour (happiness, fear, and guilt) for robots and
investigated whether participants could recognise the emotions expressed by
the robot and interact with it accordingly (Lakatos et al., 2014). The results
suggested people attribute emotions to a social robot and interact with it
according to the expressed emotional behaviour. A recent study designed
guidelines for 11 affective expressions for the Miro robot and applied
behaviour design inspired by dog behaviour among others, and evaluated the
expressions through an online video study. All expressions were recognised
significantly above the chance level (Ghafurian et al., 2022). So, the dog-
inspired behaviour proved a suitable medium for making people attribute
emotional states to a non-humanoid robot.

Another study used the behaviour of specially trained hearing dogs to design
behaviour for an assistive robot. In the study, the robot successfully led
participants to two sound sources using visual communication signals inspired
by the referential behaviour of the hearing dogs. The findings suggested that
participants could correctly interpret the robot’s intentions (Koay et al.,
2013). This study provided further evidence for dog-inspired social behaviour
being a suitable medium for communicating with human participants.

Designing robot behaviours using biologically-inspired behaviour is not
restricted to only dog behaviour. A recent study used a Roomba and a LED strip
to imitate aquatic animals’ use of light, examining how bio-luminescence,
colour science, and colour psychology can be used in HRI, with a focus on
appearing attractive or hostile (Song and Yamada, 2018). The study found that
low-intensity blue lights in a circle or split pattern were more attractive to
participants, while high-intensity red lights in a blinking or breathing
pattern were perceived as hostile (Song and Yamada, 2018). Note that aquatic
animals’ communicative signals are _not_ used in interspecific interactions
with humans and hence, have no biological relevance to human communication.
This suggests that aquatic animals may not provide a suitable model for robot
behaviour design in HRI, and explanations for these findings may be more
complex even though the signals were perceived as expected by the
participants.

Human behaviour can be a source for creating robot signals, but the result may
not always be perceived as human-like. One study used a Nao with three
presenting-behaviours:  (a) leaderwhere gaze and nod were not derived from
human response (machine-like),  (b) followerversion where the robot follows
the person’s gaze and nodding behaviour (machine-like), and  (c) semi-
followerwhere the robot would sometimes follow the person and sometimes try to
lead (human-like) (Thepsoonthorn et al., 2017).  Participants watched and were
asked to pick the most human-like behaviour. The follower and semi-follower
behaviour were picked as more human-like over the leader behaviour, but some
participants preferred the nodding behaviour of the follower behaviour, while
others preferred the gaze and face-tracking in the semi-follower behaviour
(Thepsoonthorn et al., 2017). The study showed that, given a certain context,
pursuing human-likeness may not be the most important aspect to design a
robot’s behaviour.

Human-like behaviour carries the danger that the robot may appear more
socially competent than it is. In some situations, it may be better the robot
behaves in a artificial, machine-like manner. For example, one study (Gallo et
al., 2022) had a robot with two behaviours to wait for an elevator in the
lobby of a building along with people. In the “machine-like” behaviour, the
robot waited at a designated spot and only entered the elevator after everyone
else had a spot. For the “natural” behaviour, the robot joined the rear of the
cluster of waiting people and used a (weak) first come, first served policy
that was similar to what was observed when humans wait for elevators. People
in the study felt the robot’s “machine-like” behaviour caused less confusion
than the robot’s “natural,” human-like behaviour (Gallo et al., 2022).

###  3.2. Describing human-robot communication

These examples in Section 3.1 show robot behaviours can draw inspiration from
animal, human, or machine behaviour. If we use some theory of communication or
perhaps adopt methods from the arts, we may design clearer and more
understandable signals for communication or notice potential problems.

For example, the concept of speech acts (Green, 2017) and the concept of
intentional and consequential sounds (Knepper et al., 2017) was applied to
suggest a robot’s movements are functional and communicative at the same time
(movement acts (Schulz et al., 2021)). In addition, the communicative part of
the movement has an intentional (explicit) component, i.e. designed by the
roboticist, and a consequential (implicit) component, i.e. how the person
observing a robot’s motion interprets that motion (Schulz et al., 2021). The
explicit and implicit components are split between the roboticists and the
people interacting with the robot respectively. For example, a robot’s motion
could be designed to be purely functional, but an observer can implicitly draw
unintended meanings out of it (e.g. a Fetch robot’s movement to reset its
navigation stack leads to people interpreting its movement as confusion or
being extra careful).

Designers have deliberately used explicit and implicit elements in the
movement of a robotic ottoman to indicate it wanted people to interact with it
(Sirkin et al., 2015). In another study, the researchers used artistic
techniques from improvisational acting to have the robot ottoman’s motion
interpreted by observers as having different levels of dominance (Li et al.,
2019). Designers’ attempts may not always be successful and a designed
behaviour can be misinterpreted, (e.g. a Cozmo robot’s behaviour indicating it
was done waiting for a fist bump was misinterpreted as the robot being sad for
other reasons (Pelikan et al., 2020)). It is ultimately the person interacting
with the robot who interprets the implicitly sent message, but there seems to
be a split between an _intended_ implicit signal versus an _unintended_
implicit signal.

Turning to the arts, such as Laban notation for dance (LaViers et al., 2014)
and animation principles and techniques (Schulz et al., 2019; Takayama et al.,
[n.d.]), can provide inspiration for an intentional, implicit interaction.
Bianchini et al. (2016) used Laban notation as inspiration for creating a
notation system for non-anthropomorphic robot movement, focusing on the qualia
of gesture: from movement to gesture, and from gesture to behaviour. They
noted it was difficult to determine what is or is not a gesture: a gesture
cannot be separated from its “… relation to the postural, dynamic, and
contextual organisation of the body” (Bianchini et al., 2016, p. 16).

Movement acts are valid, important classifications that contribute to the
understanding and design of robot communication with humans. Using methods
from the arts, such as animation and dance, can be a good way to implement
some of these signals. But there is a gap in distinguishing what is
intentionally implicit (that is, created by the designer to be understood
implicitly) and unintentionally implicit (that is, not created intentionally
by the designer, but still communicates something to the human). This split
needs to be better developed and presented to minimise misinterpretation.

There have been attempts at describing how communication between humans and
robots works. Much of the foundation is from two models. This first is Shannon
(1948) that creates a probabilistic communication model that can model signal
noise and modulate emphasis in communication. The second is Watzlawick et al.
(1967) and the concept of non-action summarised succinctly as “… no matter how
one might try, one cannot _not_ communicate. Activity or inactivity, words or
silence all have message value” (Watzlawick et al., 1967, p. 30). One model by
Bonarini (2020) examines the channels of hearing, sight, and touch that are
available to humans for communicating with robots and suggests to use machine-
learning techniques to teach robot behaviours and then benchmark their
performance (Bonarini, 2020). This can provide a starting point to explore
communication channels.

A more in-depth review of communication theories in HRI argued that an
asymmetrical model for communication is a better fit for human-robot
communication since robots are not human and interact with the world
differently than humans (Frijns et al., 2021), which is compatible with the
notion of type II communication (c.f. Sect. 2.1). The review presents the
AMODAL-HRI model, which focuses on actions to build a common ground between a
human and a robot (Frijns et al., 2021). It also includes the processes in the
human and robot for interpreting and acting on the situation (Frijns et al.,
2021). The model includes guidelines based on Norman’s design principles for
interaction design (Norman, 2002).

Another way to describe the communication is to create a typology for
communication for HRI. Similar to our approach, the typology created by Hegel
et al. (2011) was strongly inspired by communication theories between animals.
Signs are the basic term for an entity that transmits information to another
entity (Hegel et al., 2011). Signs could be divided into signals, the signs
acted on to alter the behaviour of another organism, and cues, any feature of
the world that a receiver could use as a guide to get information about the
signaller (Hegel et al., 2011). These signals and cues could be further
classified as human—resembling human behaviours and features—or
artificial—non-human-like signs (e.g. blinking lights or jerky movement) that
could communicate information (Hegel et al., 2011).

In their typology, Hegel et al. emphasised that signals are _explicitly_
designed by the roboticist, while cues may be explicitly or implicitly
interpreted by the receiver. That is, there is a distinction between the
active nature of a signal and the passive (evolved) nature of a cue (Hegel et
al., 2011). For example, the active nature of deliberately sending a signal by
an animal has a cost (e.g. a predator may also see the signal), and the
passive nature of _cues_ are usually evolved features like colour or patterns
that can indicate toxicity.

We argue that distinguishing between signals and cues in the design of robots
adds complexity that is not helpful since a robot’s appearance (passive cues)
and behaviour (active signals) are created by roboticists for communicative
purposes in a similar time frame. That is, the signals and cues are not
necessarily “evolved” in the traditional sense. Moreover, distinguishing
between the explicitness or implicitness in the _design of signals_ by the
roboticist may be mistaken for the explicitness of _the signal itself_ (i.e.
whether the signal needs interpretation by the receiver or not). In the
suggested new typology, we address both these problems by focusing on
communicative signals that might be intended or unintended and at the same
time contain an explicit or implicit component.

##  4\. A new typology of communicative robot signals

In this section, we introduce a new typology for communicative robot signals
in HRI that aims to support roboticists in designing and evaluating robot
behaviour. Since the suggested typology heavily relies on ethological
communication theories and draws inspiration from some earlier attempts at
applying communication theories to HRI, we will first describe how the
typology in general relates to existing theories and frameworks that consider
communication as introduced in Sections 2 and 3. We will then present how the
typology looks in detail, proposing a selection of core properties to describe
communicative signals and discuss how our suggested worksheet can be used to
apply the typology to characterise an example behaviour. The remainder of the
section will argue how the typology can guide new robotic behaviour design and
the analysis of already conducted interaction studies.

###  4.1. Scoping considerations

We adopt Slater’s view that communication is a behaviour that alters the
behaviour of an interaction partner (Slater, 1985). Contrarily to some
definitions in ethology (see Sect. 2.1), we neither require communicative
signals to be evolved to be regarded as communicative nor do we require
intentionality to consider a robot as a communicator. Moreover, we consider
communication between robots and humans to be asymmetrical, similar to type II
communication between different species (Csányi and Kampis, 1988).

We want to stress that our notion of communicative signals also includes non-
actions (Watzlawick et al., 1967) like interruptions of robot movements, which
sometimes happen deliberately, for example, to allow for human conversation,
object recognition, or position estimation. Moreover, the typology also covers
and directly addresses non-obvious (implicit) signals and overloaded signals
carrying multiple messages. We would further like to emphasise that we do not
see communicative signals in isolation but rather embedded in an interaction
(Holthaus and Wachsmuth, 2021), potentially involving multiple parties, and
complex turn-taking behaviours and feedback channels, as described by DeVito
(1986). However, to individually characterise communicative signals, we focus
on a specific view considering a single robot as the sender and a single human
as the receiver. This allows us to relate the typology to communication models
introduced in Section 3.2, e.g. Shannon (Shannon, 1948), while preserving the
potential to consider multiple receivers or swapping roles.

While the proposed typology might be applicable to either the robot or the
human as a sender, for simplicity here, we take a unidimensional perspective
to discuss communicative signals in the context of particular robot
behaviours. With that, we aim to provide a structured approach to interaction-
centric robot behaviour design and analysis in light of the asymmetric nature
of communication between humans and robots. We further acknowledge—but do not
further discuss—that communicative signals can be affected by (external)
noise, which might distort the message content.

###  4.2. Characterisation of robot signals

Figure 1. Typology of communicative robot signals in the context of robot
behaviours where signals are elicited by perceptible actions required to
fulfil a functional goal. Communicative signals can be characterised using
five properties.

This figure displays the typology as a directed graph with five nodes. The
robot behaviour node points to a functional goal node, annotated with “fulfils
0 to N”, which itself points to a perceptible action node in a “requries 0 to
N” relation. The latter points to the communicative signal node, as “elicits 1
to N”. Within the communicative signals node the five properties origin,
deliberatenes, reference, genuineness, and clarity as well as their
expressions are listed.

We define a robot behaviour as a higher level function aimed to fulfil a range
of functional goals, which in turn might require certain robot actions that
are perceptible by a human user (Fig. 1). Each of the actions might have a
number of components that the robot uses to reach the functional goal,
potentially perceptible via a range of modalities. Such a robot action always
elicits one or more communicative signals as long as a human user perceives
it.

Like other animals, humans perceive communicative signals using multiple
modalities including seeing, hearing, and touch (Goodrich and Schultz, 2008;
Bonarini, 2020). The typology focuses on structuring the content and
properties of such non-verbal messages, i.e. describing what a signal might
reveal about the robot or the environment and how the signal might be
interpreted by the human (Sect. 3.2). We therefore propose to individually
characterise communicative robot signals in the context of HRI, addressing
qualities of the sender and how it produces the signal, the signal’s message
content, and how much interpretation is required at the receiver’s end. While
we focus on five core properties, the typology is meant to be extensible and
welcomes others to define and discuss additional properties. Some of the
propositions have proven to be useful when characterising human (or animal)
signals, others are novel suggestions that arise from the specific nature of
HRI where roboticists have control over the design and expression of all robot
behaviour, including communicative signals.

####  4.2.1. Origin

Designing social behaviours is complex with formal generation still requiring
manual fine-tuning of signals (Sequeira et al., 2016). However, solutions for
manually signalling an intended meaning with a robot can often be derived from
biology (Sect. 2.2). It might be worthwhile to consult ethology when assessing
whether a signal is expressed correctly or can efficiently transport the
intended meaning. On the other hand, imitating biology when artificial
solutions are proven to be effective might lead to misunderstandings or
increase design efforts unnecessarily. Hence, evaluating a signal from both
perspectives allows roboticists to easily identify potential for improvement
or reveal unintended side effects.

With the new typology, we thus suggest to determine the origin of a signal,
i.e. whether the signal mimics communication between humans—or animals—or
whether it has been designed otherwise to understand and model communication
from the sender’s (robot) perspective. We thereby encourage roboticists to
reason about a signal’s origin and explicitly think along both alternatives
and what implications they entail with regard to signal design and evaluation.

Signals might have be derived from human communication but adapted to suit the
needs of human-robot interaction, allowing this property to have three values:
biological, artificial, or hybrid.

####  4.2.2. Deliberateness

Consequential signals are a common source of misunderstandings (Schulz et al.,
2021; Moore and Ju, 2018; Frid and Bresin, 2022; Trovato et al., 2018) because
the designer cannot control what information is being sent and is often
_unaware_ that information is being sent at all. We therefore propose to
capture whether a signal has been produced deliberately or whether it is
merely the accidental byproduct of an action to allow roboticists to identify
and reduce unwanted effects in the interaction partner. The identification of
unintended signals might guide the design process of robots or the examination
of unexpected user behaviours. One possible aim could be to eliminate
unintended signals; another one would be to identify the signal’s nature and
use them deliberately.

Assessing the signal’s deliberateness allows roboticists to further
investigate a signal from the sender’s perspective. With this typology, we
encourage roboticists to carefully consider whether perceptible actions only
elicit intentional signals or whether they might result in a signal that is
unintended. A single action may elicit more than one signal, possibly
resulting in unintended secondary meanings, depending on robot state, context,
and receiver. This property can have two values: intentional or consequential.

####  4.2.3. Reference

Robot signals with a primarily social function are often designed to reveal
some piece of information about the robot’s inner state to its human users
(Fiore et al., 2013; Holthaus and Wachsmuth, 2021; Esposito and Jain, 2016;
Fischer et al., 2019). We argue that assessing the motivational reference of a
signal, similar to ethological approaches (Kampis, 1991), helps roboticists to
determine whether the signal appropriately reveals or perhaps fails to reveal
any of the robot’s inner states. Moreover, by considering the reference of a
signal, we aim to foster the identification of additional information that
might potentially be carried by a signal. Referential signals like deictic
gestures and functional behaviour, e.g. delays or pauses, also have a
(possibly consequential) non-referential component that provides human users
with information about the robot and shapes their expectations. Sophisticated
voice or advanced arm movements when giving directions might suggest other
parts of the robot, such as its perception modules, are advanced while in
reality, they might not be.

When looking at the signal’s message content we suggest determining a signal’s
reference, i.e. whether it considers information about the sender and its
inner state or alternatively any outside entity. This allows us to distinguish
between non-referential and referential communicative signals in a similar way
that ethology does (Kampis, 1991) for assessing the effectiveness of a signal.

####  4.2.4. Genuineness

Many communicative robot signals are deceptive in their nature (Sharkey and
Sharkey, 2021) and often suggest non-existing robot capabilities to a human.
Displaying emotion, for example, dishonestly implies that a robot would be
able to feel them. An analysis of this property might therefore help to
evaluate positive and negative effects on the receiver, as well as potential
harmful effects on the interaction like eliciting wrong assumptions or false
expectations (Rosén, 2021) and generating inappropriate relationships between
human and robot (Ososky et al., 2014; Salem et al., 2015). Whether a signal is
honest or deceptive is taken from the perspective of the robot, the
information it has, and is independent of the _truth_ or facts of the
interaction. Missing information, failures in the accuracy of sensors, etc.
changes whether the information communicated by the signal is true or false.
One could debate whether such a truth is possible in all situations, but that
debate is beyond the scope of this paper.

Another important dimension of a communicative signal, relating mostly to its
message content, is therefore its genuineness. Signals can either be honest or
deceptive. Determining the genuineness of the signal can promote discussing
whether a signal, perhaps intentionally, misleads the receiver or whether it
communicates honestly and shapes user expectations as desired.

####  4.2.5. Clarity

Implicit signals are frequently applied to support interactive robots subtly
transporting meaning, for example, to request help from a human in a
collaborative scenario (Cha and Matarić, 2016) or by using social proximity to
support interaction opening (Holthaus and Wachsmuth, 2021). Implicit signals
account for the majority of communication in some situations, such as crossing
intersections in traffic (Lee et al., [n.d.]; Zhang et al., 2022), which
stresses the need to identify and characterise them when considering
autonomous vehicles as well as HRI. Implicitness of a signal can lead to
ambiguity and therefore can provide flexibility and adaptability to the
current situations, allowing humans to interpret the exact meaning of the
signal (c.f. (Pelikan et al., 2020)). On the other hand, such signals are
often more difficult to decode than explicit signals (Maynard Smith and
Harper, 2003), especially when considering the diversity of humans, coming
from different backgrounds and bringing different expectations.

When considering the reception of the message, the typology therefore suggests
to determine a signal’s clarity. This property can have two values: explicit
or implicit. That is, we aim to estimate whether the signal is sufficiently
clear so that it can be understood without much effort or whether it is
subtle, leaving room for or requiring interpretation by the receiver.

###  4.3. Application of worksheet

Figure 2. Recommended worksheet to use in robot behaviour design or analysis
applied to an example behaviour where a receptionist robot gives directions to
a visitor. Shaded entries denote areas where the attention of a roboticist
might be required.

This figure displays the worksheet as boxes where the nodes of Figure 1 are
used as headings for boxes. Two boxes “perceived information” and “human
behaviour” are added. Boxes are summarising the text in Section 4.3 where the
”robot behaviour” box is filled with ”Giving directions to a visitor”, two
functional goals and three observable actions are listed as a triplet
consisting of component, modality, and activity (with the audio modality of
the pointing gesture and verbal explanation shaded to indicate potential
problems). The “communicative signals” box lists two signals with the message
content “location of waypoint or target” and “deterioration or damage”
resulting from the pointing gesture and displays checkmarks or crosses for the
properties of each signal to indicate their expression, using symbols for
biologically-inspired and artificial. Moreover, “uncertainty” is listed
(shaded) as potentially problematic perceived information in the human,
together with “hesitation” as a human behaviour.

The typology of robot communicative signals is meant to be workable,
extensible, and applicable to similar kinds of communication. For that, we
introduce a worksheet (Fig. 2) that may be used to apply the typology in the
behaviour design and evaluation processes.

As an initial step for using the worksheet, we suggest to identify individual
robot behaviours within a certain scenario, i.e. higher-level functions to
fulfil a range of functional goals. For each behaviour, the worksheet is then
recommended to be filled left-to-right but please note that the filling order
highly depends on the use case, for example, one might want to start on the
right, denoting any human behaviour when analysing surprising events. We would
like to discuss the worksheet alongside a simple example where a humanoid
receptionist robot engages in a behaviour that explains directions to a
visitor. We can therefore start filling the worksheet with capturing the
_robot behaviour_ as such. The according _functional goals_ would be to
communicate the room’s physical location within the building and enable the
visitor to find that room.

Then, the behaviour should be decomposed and partitioned into actions. This
can be achieved by individually looking at each component involved in the
behaviour and looking at the sequence of perceivable actions, including
pauses, carried out by that component during the behaviour. Here, the
direction-giving behaviour happens immediately after a human asks for a
location. It is composed of a gaze shift moving the head and a pointing
gesture with the arm that can be visually perceived. Both movements are also
presented with a noise originating from the motors and moving parts.
Simultaneously, the robot verbally explains where the room is located and what
waypoints to find along the path.

All three actions are designed to multimodally contribute to the message
content of explaining the location of waypoints or the final target. For
simplicity, we continue the worksheet characterising only the properties of
the pointing gesture, omitting the gaze shift (functioning as intended), and
the verbal explanation (implemented in a sub-optimal way, e.g. without lip
synchronisation).

The pointing gesture would usually be modelled in a human-like (biological)
fashion, whereas the noise of moving mechanical parts or motors is artificial.
This would imply that the movement would likely be interpreted in comparison
to human movement, highlighting the importance of eliciting a smooth
trajectory that is biologically plausible. Jerky or unnatural movements on the
other hand might suggest a faulty robot, potentially eliciting association
with illness (Langer and Levy-Tzedek, 2020). A receptionist robot would
generate a pointing gesture intentionally to support its verbal explanation
about the route. The presented noise, on the other hand, is often an unwanted
consequence of moving parts, likely causing problems in the interaction where
unwanted signals are communicated to the human; for example, when exaggerated
or distorted noise is produced due to insufficiently calibrated hardware.
Pointing gestures are inherently referential whereas emitted noise solely
informs about the robot’s current condition and is hence non-referential. A
closer look at what the noise could communicate about the robot might help to
explain unexpected human behaviour. Excessive noise, for example, might
communicate deterioration or damage whereas the normal motor sound would
appropriately signal the robot’s inner workings and capabilities. Since, in
this example, the receptionist robot is designed to be helpful, it would
produce an honest gesture, pointing in the correct direction to the best of
its knowledge. If desired, however, such a gesture could be generated to
intentionally mislead a human user. Consequential noise is not created on
purpose and therefore also usually honest. Pointing gestures are often
explicit, especially when displayed while giving directions. However, if the
sender and receiver used different reference systems, the gesture would become
ambiguous and hence less explicit. Likewise, a squeaking noise can explicitly
signal that robot parts are grinding against each other whereas other noise
might be more difficult to interpret and thus be more implicit.

Assessing the properties of the communicative signal allows us to estimate the
information that is being perceived by the human user of the robot. In this
example, the user notices that the robot explains directions, which is
facilitated by all three actions. However, excessive noise while presenting
the gesture might cause uncertainty about the capabilities of the robot and
the accurateness of its actions. An expected (or observed) human behaviour may
therefore include signs of hesitation or requests for confirmation.

###  4.4. Using the typology in behaviour design

Perceptible robot behaviour results from design choices at different levels of
abstraction, ranging from low-level hardware design to the definition of high-
level behavioural goals, which may involve a sequence of actions or a
combination of actuators. At all levels, roboticists have the opportunity to
influence the emerging robot behaviour so that it is, to some degree, shaped
by a designer to perform a confined set of functions. Robot behaviour,
however, is also dependent on perception and embedded in environment and
interaction; not every robot action is predictable. For example, robot
behaviour might be generated from a learning component adapting to user
preferences. Still, the robot’s interaction architecture, training pipelines
for machine learning, and individual robot actions are frequently hand-crafted
and can be dictated by a roboticist.

The proposed typology aims to be applicable at all abstraction levels of
robotic behaviour design by looking at the final robot behaviour from an
interactive perspective, i.e. by focusing on perceptible actions. At the same
time, it is good practice to always consider the functional goals when
designing any robotic behaviour (Miklósi and Gácsi, 2012). As described in
Sect. 4.3, the worksheet supports such an approach since it encourages
roboticists to associate each observable robot action aiming to fulfil a
functional goal as part of a high-level behaviour with communicative signals.
When designing behaviours, signals and their properties emerging from
observable actions can then be characterised in context using the suggested
terminology, which in turn allows estimating what information is transmitted
and how the human’s behaviour might be affected.

Someone might want to design an attentive behaviour for a humanoid robot that
aims to fulfil the functional goals of attracting users and keeping them
engaged during conversation. For that purpose, the robot could be equipped
with a function that makes it look towards a human, letting the user observe
robot actions where it continuously adjusts its body and head orientation as
well as its eye gaze direction towards the user and the user’s face. For the
purpose of this exercise, we assume that the only involved modality is vision
whereas the motors operate without perceptible noise.

Such activities send many communicative signals a roboticist might want to
control during the design process. All three actions can be designed to
contribute to a biologically-inspired, intentional, non-referential, honest,
and largely explicit communicative signal indicating that the robot’s
attention is focused on the human. However, a designer may want to reinforce
the signal by adding another artificial one, such as setting the robot’s eye
colour to green whenever it has detected a user in its focus. At the same
time, changes in body posture and gaze can imply consequential communicative
signals that may need to be considered. Over-commitment to a focused
interaction might be such a signal, for example when orienting the body
towards a human who is still far away or by perpetually staring at them during
conversation. Moreover, signalling attention while the robot is (temporarily)
unable to interact might set wrong expectations and therefore become
deceptive. A robot designer could use such insights (and more) to adjust the
situations where the behaviour is being expressed to maintain the desired
human behaviour of being engaged with the robot.

###  4.5. Using the typology in existing studies

While we have provided examples of a robot pointing and adjusting its
attention, let us apply the topology to some instances from literature.

The first example is the Fetch robot resetting its navigation stack by
rotating in a circle (described in Schulz et al. (2021)). The origin of this
action is artificial and is not based on anything biological. Looking at the
rotations deliberateness, we can classify the signal as consequential since
the robot’s motion was to reset its navigation stack than communicate anything
(but it could be interpreted by an observer as intentional). The rotation
itself was non-referential since the robot’s motion is related to its internal
state of being unable to localise itself, not referencing the environment. As
to the rotation’s genuineness, we can classify it as honest; The robot was
trying to reset its navigation stack and not being deceptive. As to the
rotation’s clarity, it was clearly implicit. People observing the robot would
have no idea what it was doing unless they were familiar with the robot’s
navigation routine. Applying the new typology shows there is potential for
this motion to be misunderstood even though the behaviour serves its function.
It seems that the implicitness of the signal makes people confuse the non-
referential repair mechanism with a referential signal that aims to draw their
attention to something in the robot’s surroundings.

For the Cozmo robot signal that it did not get a fist bump in time (described
in Pelikan et al. (2020)), the origin of the action is biological (human).
Here, the deliberateness of the action was intentional, as it signalled that
the time had expired for giving a fist bump. This is a non-referential signal
as well since Cozmo signalled its “desire” for a fist bump on its front
loader. The signal was also honest, as a desire for this action has been
modelled on the robot. The clarity, at least as thought by the designers, was
explicit. The typology shows that the understanding of the signal should be
straightforward, yet the behaviour in isolation could still be misinterpreted
(Pelikan et al., 2020).

The typology can also be used to see differences in behaviour between
conditions in a study (and possibly improve the internal validity of an
experiment). For example, in the study of the robot waiting for an elevator
from Gallo et al. (2022), the origins of the “machine-like” behaviour is
artificial, and the “human-like” behaviour origin is biological, but the other
properties of both behaviours are intentional, explicit, honest, and
referential. Using the typography can show how changing one component of the
communicative signal can have an effect on the behaviour participants prefer
in a given situation.

##  5\. Discussion and Limitations

The typology provides an accessible starting point for designing behaviours or
examining how robot behaviour could be interpreted. The typology should work
for robot signals in other modalities such as virtual or augmented reality
(Walker et al., 2018). It also has the advantage that it can be compatible
with other theories and explanations while at the same time being extensible
to allow for customisation. For someone who wanted to use movement acts as a
way to explain movement, the typology provides a mechanism for breaking down
the act into implicit or explicit and intentional or consequential parts,
while also providing opportunities for adding richness in describing the
movement act (such as deciding if the act is honest, referential, etc.).
Designing signals and behaviours using the new typology can be incorporated
into other theories of communication, such as the AMODAL-HRI (Frijns et al.,
2021). In that model, our typology can provide a starting point to discuss how
the robot’s signals that can lead to joint actions with humans. This matches
suggestions that “Analysis at the level of signals and cues can serve as a
basis for discussion, but needs to go further.” (Frijns et al., 2021, p. 11).

Miklósi and Gácsi posited: “… the actual social behaviour of a robotic
companion should depend on the assumed function” (Miklósi and Gácsi, 2012, p.
2). The typology reinforces this importance of behaviour design for a
functional goal. The worksheet (Fig. 2) supports this process and facilitates
relating the functional goals with the communicative meaning of signals and
the predicted or observed human behaviour.

The clarity of a robot signal depends on any expectations that a human might
have, which might originate from the robot’s intended function or perhaps the
human’s unfamiliarity with the robot. User expectations can sometimes
determine whether certain signals have a positive effect on user acceptance
and the robot’s trustworthiness. Some people who interact with a humanoid
robot, might expect and prefer a robot to display emotions, which then in turn
increases the robot’s acceptability and trustworthiness (Esposito and Jain,
2016) even when the emotion is clearly deceptive. Human expectations thereby
vary with a magnitude of factors, which is not directly addressed in our
typology. However, Rosén et al. provide a framework (Rosén et al., 2022) that
can be considered when applying the typology in behaviour design or analysis
to estimate the effects of user expectations on the signal, the perceived
information and the human behaviour.

Pelikan et al. (2020) recommended designers explore the ambiguity of implicit
signals to enhance interaction. The new typology is limited in assessing an
exact level of implicitness, but it can provide a good place to see this
ambiguity by requiring designers to classify the clarity of the signals. We
therefore encourage designers to classify the clarity in a binary way,
however, comments or more gradual classifications might also be applied.
Moreover, diversifying participants and other stakeholders can provide
opportunities for challenging assumptions and finding solutions.

Outside the scope of the typology are any costs of the robot producing
communicative signals that need to be weighed against any potential benefit of
communicative success. In the animal world, producing a communicative signal
often means balancing between the risk of becoming prey versus increasing the
individual’s chance of reproduction (Maynard Smith and Harper, 2003). Human-
human or human-robot communication typically has much lower predatory risks,
but other costs might become relevant, e.g. when balancing attention.
Communicative robot gaze towards a person, for example, might distract from an
object classification task while providing the benefit of easily establishing
joint attention. Finally, we have shown possible applications for the typology
as an initial verification. Future verification can include HRI experiments
that examine behaviour design and communication with or without the help of
the typology.

##  6\. Conclusion

In this article, we examined how ethology could be applied to HRI, looked at
example studies that used ideas from ethology or other areas that have a robot
communicate with humans, and examined different ways of describing
communication between them. We then presented a new typology for communicative
robot signals that included multiple properties of origin, deliberateness,
reference, genuineness, and clarity. We used this typology to examine
communication from previous studies and provided a sketch to how typology
could be used for new designs.

Typologies and theories may be refined and new ones will be proposed; we
complement this typology with a worksheet that can be applied to encourage
designers and roboticists not only to think about a behaviour, but also assess
how behaviours will be perceived and understood by people interacting with
robots. Any increased focus on this aspect of HRI should result in less
confusion and more understandable interactions. This may lead to future
interactions that are more effective, acceptable, trustworthy, and pleasant.

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