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,

Human–animal 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

collaboration.

1 INTRODUCTION

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

other.

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

191

design principles and cobot-behaviour modelling

analogous to natural animal behaviour.

2 TEAMS AND TEAMWORK

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

192

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

teams.

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.

3 DESIGN PRINCIPLES FOR

COBOT TEAM MEMBERS

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

193

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.

4 MODELING COBOT

BEHAVIOUR AFTER ANIMALS

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

donkeys.

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

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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

humans.

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.

5 DISCUSSION AND

CONCLUSIONS

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

195

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.

ACKNOWLEDGEMENTS

This research is was funded by the Dutch Ministry of

Economic affairs through the SIA-RAAK program,

project “Close encounters with co-bots”

RAAK.MKB.08.018.

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