Human-cobot Teams: Exploring Design Principles and Behaviour
Models to Facilitate the Understanding of Non-verbal
Communication from Cobots
Marijke Bergman
, Elsbeth de Joode
, Marijke de Geus
and Janienke Sturm
School of HRM and Psychology, Fontys University of Applied Sciences, Eindhoven, The Netherlands
School of HRM and Psychology, Saxion University of Applied Sciences, Deventer, The Netherlands
School of Engineering, Fontys University of Applied Sciences, Eindhoven, The Netherlands
Keywords: Human-robot Interaction, Human-robot Teamwork, Cobot Behaviour, Cobot-design Principles,
Humananimal Team Metaphor.
Abstract: Now that collaborative robots are becoming more widespread in industry, the question arises how we can
make them better co-workers and team members. Team members cooperate and collaborate to attain common
goals. Consequently they provide and receive information, often non-linguistic, necessary to accomplish the
work at hand and coordinate their activities. The cooperative behaviour needed to function as a team also
entails that team members have to develop a certain level of trust towards each other. In this paper we argue
that for cobots to become trusted, successful co-workers in an industrial setting we need to develop design
principles for cobot behaviour to provide legible, that is understandable, information and to generate trust.
Furthermore, we are of the opinion that modelling such non-verbal cobot behaviour after animal co-workers
may provide useful opportunities, even though additional communication may be needed for optimal
The way factory workers work with robots in
industrial environments is changing. Robots are no
longer exclusively machines that are encaged or
otherwise separated from the work force. They now
enter people’s workspace and they are becoming co-
workers which are meant to collaborate with humans.
Hence this type of robot is called cobot, short for
collaborative robot. Currently most cobot
applications are limited to coexistence, the cobot
dwells in the same work area as humans, but it has its
own tasks and there is limited or no interaction.
However, it is to be expected that in the near future
cobots and humans will cooperate, that is they will
work on the same product in the same location.
Eventually, true collaboration may be achieved,
where cobot and human work at the same product at
the same time in close and physical contact with each
These cobots have to function in less predictable
environments than traditional industrial robots. Also,
the users of cobots might be less technically literate
than the operators of traditional industrial robots.
Therefore cobots have to be able to interact naturally
and intuitively with humans and to fit in the human
work environment (Korondi et al., 2015). Much
research on Human Robot Interaction (HRI) and
robot design focusses on service robots interacting in
social or public settings, for instance the work of
Dautenhahn (2007), Hoffman et al., (2014), Dragan
and colleagues (Cha, Dragan and Srinivasa, 2015)
and Sisbot (Sisbot et al., 2010. Insights from this
research can be useful, however applying them to
human-friendly design of industrial collaborative
robots may have its limitations and research in this
area is not as widely available (Bartneck et al., 2009;
Michalos et al., 2018; Sheridan, 2016). An important
question is how and which design principles may be
used for modelling the behaviour of robots in an
industrial setting. Since in many industrial
environments verbal communication is limited
because of factory noise, our research focuses on non-
verbal behaviour.
In our own research project we explore this
approach to promote intuitive interaction with
industrial cobots and improve collaboration in
human-cobot teams. In this paper we propose to use
Bergman, M., de Joode, E., de Geus, M. and Sturm, J.
Human-cobot Teams: Exploring Design Principles and Behaviour Models to Facilitate the Understanding of Non-verbal Communication from Cobots.
DOI: 10.5220/0008363201910198
In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 191-198
ISBN: 978-989-758-376-6
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
design principles and cobot-behaviour modelling
analogous to natural animal behaviour.
The developments in industrial work environments
imply that humans have to form teams with cobots,
their new co-workers. To better understand what is
needed to cooperate and collaborate with cobots, it is
important to look at some key concepts of teamwork.
2.1 Human Teams
There is ample research concerning human teams and
teamwork resulting in more than 130 models on
teamwork (Salas, Cooke and Rosen, 2008). In spite of
this variety in models, one can derive some common
characteristics and concepts. Most approaches
consider a team to be a group or unit with members
with high task interdependency and shared and valued
common goals. Members of a team have to share,
integrate and synthesize information. Also they have
to work together and coordinate their work to reach
common goals (Costa, 2003). Thus, important
concepts are (1) interdependency, i.e. failures of one
member have effects on the work of others; (2)
sharing information, which is necessary to guarantee
a smooth process; (3) coordination and cooperation to
reach common goals.
Interdependency occurs during the process of team
performance, i.e. the process in which tasks are
carried out. Such tasks may be performed
independently (task work) or interdependently
(teamwork). Teamwork concerns the interdependent
components of performance required to effectively
coordinate the performance of several team members
(Salas et al., 2008). In addition, Costa (2003) stresses
that task interdependence is required “such that
individuals need to develop share[d] understandings
and expected patterns of behaviour” (p.606).
Coordination is important for teamwork as well.
Members of human teams provide feedback on
whether the messages of other team members are
received and understood or on the status and progress
of the process at hand. Team members anticipate each
other’s need for information and provide such
information proactively (McNeese et al., 2018). Some
of the information may be verbal, but there will be a
fair amount of non-verbal communication.
In well-functioning teams Sharing Information
and coordination seem to take place with hardly any
explicit verbal communication. This can be achieved
because such teams have shared mental models, i.e.
corresponding ideas on the work at hand and how to
perform this work. Shared Mental Models and team
cognition help team members anticipating as well as
executing actions (Kozlowski and Ilgen, 2006).
In short, in the case of effective team performance
team members cooperate and collaborate to attain
common goals. Consequently they provide and
receive information, often without using language,
and coordinate their activities, resulting in a fluent
intertwining of these activities.
Working as a team requires a willingness to
cooperate. Research on human teamwork shows that
Trust plays an important role in optimal cooperation
and collaboration (see for instance Axelrod, 1984;
Mayer et al., 1995; McAllister, 1995, Sheng et al.,
2010). Humans infer trustworthiness from the
behaviour and actions of others. As is often said
actions speak louder than words. Experiential trust
between people develops if one can be sure that one
can count on a partner, that his or her behaviour is
logical, predictable and consistent, and that he or she
means well (Mayer, Davis and Schoorman, 1995).
Such experiential trust consequently is context
dependent, one trusts a team member on the basis of
his behaviour in a specific work setting. Trust is
found to affect effective performance, satisfaction
with the team and commitment (Costa, 2003). It may
be clear that trust and optimal teamwork depend on
the interaction and communication, whether it be
verbal or nonverbal, between team members.
2.2 Teaming with Cobots
Given that cobots will work closely together with us,
it may be assumed that several principles of human
teamwork will apply in this situation as well. In
research on human-robot collaboration the concepts
of interdependency, trust, communication and
coordination are studied to some extent.
Interdependency in human-robot teamwork is
studied by Johnson and colleagues (Johnson et al.,
2011; 2012). They point out that robots may be
capable to execute individual tasks autonomously, but
that in joint activities team members have to be aware
of each other’s states and actions. is required.
Furthermore, careful orchestration of the transfer of
tasks as well as continuous interaction to perform
shared tasks are needed. This implies that it should be
transparent what a cobot is doing and why.
Trust has been identified as an important element for
the success of human-robot teamwork
(Charalambous et al., 2016; Marble et al., 2004).
Research on trusting robots shows that humans
should be able to trust that a collaborative robot does
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
not harm their interests and welfare. The factors to
build trust are mostly related to performance factors
of the robot, such as the behaviour, reliability and
predictability of the robot, and robot attributes, such
as proximity and (assumed) personality (Hancock et
al., 2011; van den Brule et al., 2014). In this sense
trust in robots parallels experiential trust in human
With regard to Coordination, Christoffersen and
Woods (2002) state that any automated system should
cater for fluent and coordinated interaction, and
should be a true team player. A breakdown in
coordination will lead to accidents. As in human
teams, good coordination depends on sharing
Information in a timely and understandable manner.
Just as in human teams, this information need not be
linguistic. The work of Hoffman and Breazeal (2007;
2010) shows that the fluency of interacting
behaviours between robots and humans can be
enhanced if robot behaviour is designed in a way that
its actions can be anticipated by humans.
However, the way a cobot communicates may not
be the way humans are used to and readily
understand. This is why Lichtenthäler and Kirsch
(2016) call for “legible” behaviour, that is, behaviour
that will help humans to understand the robots
intentions. Like in human teamwork, much of the
communication will be non-verbal, through the
design and movements of the cobot.
Whether intentional or not, the looks and behaviour
of a robot, or cobot for that matter, provide
information. Humans will try to interpret this, often
non-linguistic, information. Moreover, they tend to
attribute life to non-living objects (animism) and to
interpret the behaviour of such objects in human
terms (anthropomorphism) (Guthrie, 1993; Korondi
et al., 2015). It may be assumed that the way humans
experience the behaviour of and interaction with a
robot will have important effects on team
performance and individual wellbeing.
Research outcomes on robot behaviour can be
transferred to cobot design without much problems
since a cobot essentially is a robot that is limited in
speed and strength. Robot studies show that the
predictability of robot motions influences human
task-performance and user experience: lower
predictability results in lower performance
(Koppenborg et al., 2017) and lower experienced
comfort (Butler and Aga, 2001; Tan et al., 2009).
Furthermore perceived safety and trust may vary
depending on whether a robot, either social or
industrial, meets the expectations of humans (Rios-
Martinez, Spalanzani and Laugier, 2015; Eder,
Harper and Leonards, 2014). This implies that
deliberate design principles are necessary to
consciously design the cobot and its behaviour to
facilitate interaction and teamwork.
The design of communicative behaviour of social
robots is often based on human-human interactions
(Takayama, Dooley and Ju, 2011; Kittmann et al.,
2015). Since human behaviour is well known and
readily interpreted by others, it is assumed that
imitating human behaviour, specifically motions, will
help to understand and predict the actions of a robot
(Lichtenthäler and Kirsch, 2016). For instance, Castro
Gonzales et al., (2015) show that naturalistic
movement makes an animate impression and
increases the likability of a robot. Also, adding social
cues, such as acknowledging a user by nodding, have
been shown to help to enjoy working with the robot
(Elprama et al., 2016).
Furthermore, in human-human interaction posture
and movement are perceived as having meaning and
intent (Pollick et al., 2001). Making industrial robots
or cobots move like humans is not always feasible,
however. The non-verbal behaviour a robot can
display by its movements, is mainly determined by
technical constraints and safety guidelines. This may
cause limitations in realizing subtle details of
movement in mechanical agents, despite the progress
that is made in cognitive engineering to improve the
interdependency in a human-cobot team.
In the design of social robots as well as cobots a
head is often suggested to increase likability. People
tend to look for and see a face in almost anything
(pareidolia). This evolutionary feature is based in a
network of cortical and subcortical regions
(Hadjikhani et al., 2009), and is believed to enable
humans to detect whether a person (or animal) is kind
or angry and to detect danger. The face and the head
are used as a focal point for interaction, it shows one
where the attention of another creature is directed and
helps one to infer intentions. In line with this
approach gazing behaviour is used in robots, for
instance to refer to objects or locations or to establish
attention. However implementing human eye
movement in robots is not always feasible because of
cost and the needed degrees of freedom for such
movements (Admoni and Scassellati, 2017).
Furthermore, if an interface, like a screen, is used to
display the gazing behaviour, this may distract the
user from the task at hand.
Human-cobot Teams: Exploring Design Principles and Behaviour Models to Facilitate the Understanding of Non-verbal Communication
from Cobots
A promising approach for designing interaction
can be found in the tradition of animation (Lasseter,
1987; 2001). Animation helps to bring non-living
objects to life. Though human behaviour is often used
to animate object, this is not absolutely necessary.
Behaviour found in animals and nature in general can
be useful as well. Applying animation principles is
found to be successful to increase likability and
intuitive understanding. This approach was taken up
by several researchers to animate robot behaviour
(e.g. van Breemen, 2004; Hoffman and Ju, 2014;
Saldien et al., 2013). For instance, the path of a
movement becomes more predictable by using arcs.
Usually the movements of natural objects, animals
and humans follow an arched trajectory, whereas
mechanical movement proceeds in straight lines. Also
anticipation may be used to announce an action, like
a person bending his knees before jumping or a
baseball player who moves his arm back before
making a pitch.
An important condition to allow for
understanding and using intuition is that the character
and behaviour of the robot are coherent. Since the
appearance evokes expectations, emotions and
interaction affordances (Hoffman and Ju 2014), the
actual behaviour the robot displays should be
coherent with and follow logically from its
appearance (de Geus, 2017). Therefore, conscious
and thought-out application of animation principles is
required to match expectations and reflect the actual
possibilities of a robot. Though animation seems a
promising approach, it is essential to carefully
consider which type of behaviour the robot can best
be modelled after.
Modelling robots and cobots after humans may have
drawbacks. Many believe that increasing the
similarity of robots to humans will increase the
chances that humans refuse interaction with or
become frightened of very human-like agents. This
so-called Uncanny Valley (Mori, 1970, essay
translated by Mori, MacDorman and Kageki, 2012)
seems to hold for western cultures in particular
(Kaplan, 2004). Although recent developments in
cognitive engineering and deep learning make it more
feasible for cobots to become anticipatory, i.e. to
understand the world and humans around them and
act accordingly, today’s robotics is not yet advanced
enough to reach the physical and cognitive
capabilities of humans (Korondi et al., 2015).
Therefore, making a robot look and behave like a
human may cause a mismatch between perceived and
true capabilities of the robot (Cha et al., 2015). This
means that the physical embodiment should not
transcend the true capacities of the robot, and its
behaviour should faithfully mirror its actual skills, be
it mental or physical. To imply higher can result in
disappointment, and a decrease in believability (Rose
et al., 2010). Also, giving a cobot a face may divert
the focal point of attention away from its actuators. If
the use of humanlike faces or eyes has no further
meaning, the resulting distraction increases cognitive
load and hinders task execution.
These drawbacks lead us to agree that an
alternative is needed for designing the behaviour of
cobots. Drawing on human-animal interaction may
offer such alternative. A note of caution is in place
here, since a cobot cannot exhibit the full spectrum
of behaviour of the model animal, just as it cannot
fully imitate human, non-verbal behaviour. Yet, in an
industrial setting, simple animalistic analogues may
be helpful.
Looking at human-animal interaction has been
suggested in literature, for instance by Phillips et al
(2012) and Koay et al (2013). Historically, the most
common human-animal teaming is focussed on
replacing, augmenting or multiplying the physical
capabilities of humans (e.g. horses, elephants, oxen,
and dogs). This is similar to the use of robotics in an
industrial environment. Robotic arms are used to lift,
transport, and manipulate objects with a stamina and
strength not present in humans. This is akin to the
tasks mankind transfers to stronger animals, like
elephants or horses. Automated Guided Vehicles
(AGVs) or transport robots can fetch and transport
products, which is similar to working dogs and
Using an animal metaphor has some important
advantages. For one thing, mankind’s history of
successful and continuing cooperation with animals
can be of use to facilitate the interaction with cobots
(Phillips et al., 2012). Metaphors serve to provide
familiar entities that enable people to readily
understand the underlying conceptual model and
know what to do with a technology, how to approach
it, what they can expect from it, in short; how to use
it (Sharp, Preece and Rogers 2019).
Also, humans learned to interpret or read the
behaviour of many animals. There is an intuitive
understanding of what an animal communicates.
Humans have developed the ability to form social
contact with many creatures, in which signalling
behaviour partly overlaps, and to use this in a
CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications
cooperative setting. This means that the so needed
“legibility” of robot behaviour (Lichtenthäler and
Kirsch, 2016) may be improved by mimicking animal
behaviour. Etho-robotics research, for instance, uses
ethological principles and methods based on the study
of animal behaviour, specifically in their natural
environment, to derive complex behavioural models
which can be implemented into robots (Korondi et al.,
2015). The etho-robotic approach further stresses the
strong functional relationship between embodiment
and behaviour.
Furthermore, shaping robots in animal form or
terms, zoomorphism, may help to activate existing
mental models and to build new mental models of
cobots (Phillips et al., 2012). As an extension thereof,
biomimicry, imitating natural forms and processes
(Benyus, 1997), can be taken as inspiration, like for
instance the new Handle box handling robot,
developed by Boston Dynamics that has bird-like
features and behaviour.
Another advantage may be, that in contrast with
cooperation between humans, for many forms of
cooperation with animals it seems that an animal
would not need mental representations (Gärdenfors,
2008). If there is common goal in the physical world
such as finding food or averting danger, the
collaborators do not necessarily need not have a joint
representation before acting. Only when future and
hypothetical goals have to be achieved, shared mental
models are required. Thinking and planning beyond
the present seems to be unique to humans and some
hominoids like chimpanzees or orang-utan. Animals,
and in our view also cobots, may not need to have a
theory of mind for successful cooperation with
Using the working animal metaphor can thus
provide insight in the design of cobots and the
training of humans who need to interact with them,
because it taps into well-established mental models
and human tendencies. Still, just as with modelling
after humans, careful attention should be given to the
chosen behaviour and consistency with the intended
purpose of the cobot.
The literature on teamwork shows that coordination
and communication between team members are
important to make teamwork effective. These aspects
also help to build trust between team members. As
was demonstrated this applies both to human teams
and human-robot teams. Therefore facilitating
communication and understanding the actions of
cobots are important aspects of implementing cobots
in an industrial environment. In this paper we
focussed on non-verbal communication of the cobot.
This does not, however, imply that all communication
should be non-linguistic. Yet, as it is the cobot’s
primary way to communicate with humans, these
humans have to find a way to interpret its, mostly
non-verbal, behaviour. We claim that to make the
behaviour of the robot more transparent and legible,
one needs carefully thought out design principles.
The tradition of animation can help to design such
legible robot behaviour.
Models from which to deduce this behaviour can
be found in the animal world rather than in human
behaviour. This because mimicking human behaviour
can be both misleading and uncanny since
mismatches in both physical and mental capacities
may be lurking. Experience in working with animals
has taught humans to interpret and predict their
behaviour and to estimate what they are capable of.
Yet there may be complications with this approach if
the context of use and the purpose of the design are
not carefully considered. For instance, the design of
a cobot may still suggest that it is strong while it
actually is weak or vice versa. A design primarily
aimed at evoking emotions and to look cute will not
be very useful if the robot is to be employed in
dangerous environment. In short, form and behaviour
should still follow function. Therefore careful study
of the situation and matching behaviour is essential.
As developments in for instance artificial intelligence
and deep learning are progressing, implementing
more human-like behaviour may become feasible.
Yet, one should still carefully consider which
affordances are to be suggested by the design.
One of the issues that was not addressed here,
concerns implementing cobots in industrial
environments and training human co-workers.
Humans need to build accurate mental models and
trusting relationships. These can arise through
exposure, experience building or more explicit
training methods. Here training designs that resemble
animal training/familiarization paradigms in which
humankind has longstanding experience (Phillips et
al., 2012) can be of use. Several assumptions could be
beneficial for safe cooperation with industrial cobots.
For instance, in the cooperation with animals one
assumes that one needs some form of training, or at
least experience, to work with them. Similarly, it may
also be advantageous to make use of established
training paradigms for human-animal teams (Phillips
et al., 2016). Also, there is a respectful distance
Human-cobot Teams: Exploring Design Principles and Behaviour Models to Facilitate the Understanding of Non-verbal Communication
from Cobots
humans keep from larger working animals, knowing
their physical strength is superior to ours (just like
most industrial robots).
More research is needed to determine which
models for cobot behaviour in industrial settings are
most appropriate to ensure intuitive interaction and
cooperative non-verbal communication in specific
work contexts. Grounded use of cobot behaviour can
bring safe and seamless interaction between human
and cobot closer and make true teamwork possible.
This research is was funded by the Dutch Ministry of
Economic affairs through the SIA-RAAK program,
project “Close encounters with co-bots”
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