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More Than the Sum of its Parts: Assessing the Coherence and Expressivity of a Robotic Swarm


Abstract and Figures

The robotics community is considering the use of large groups of robots, also known as artificial swarms for applications in unknown and dynamic environments. In this context, swarms of robot will need to interact with users to accomplish their mission. Unfortunately, little is known about the users' perception of group behavior and dynamics, as well as what is the best interaction modality for swarms. In this paper, we focus on the movement of the swarm as a group to convey information to a user: we believe that the interpretation of artificial states based solely on the motion can lead to promising natural interaction modalities. We define the expressivity of a movement as a metric to understand how natural, readable, or easily understandable such movement may appear. We then correlate expressivity with the control parameters for the distributed behaviour of the swarm. A user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system.
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More than the sum of its parts: assessing the coherence and
expressivity of a robotic swarm
Florent Levillain, David St-Onge, Elisabetta Zibetti and Giovanni Beltrame
Abstract The robotics community is considering the use of
large groups of robots, also known as artificial swarms for
applications in unknown and dynamic environments. In this
context, swarms of robot will need to interact with users to
accomplish their mission. Unfortunately, little is known about
the users’ perception of group behavior and dynamics, as well as
what is the best interaction modality for swarms. In this paper,
we focus on the movement of the swarm as a group to convey
information to a user: we believe that the interpretation of
artificial states based solely on the motion can lead to promising
natural interaction modalities. We define the expressivity of a
movement as a metric to understand how natural, readable,
or easily understandable such movement may appear. We
then correlate expressivity with the control parameters for the
distributed behaviour of the swarm. A user study confirms the
relationship between inter-robot distance, temporal and spatial
synchronicity, and the perceived expressivity of the robotic
As robots make their way into our world, the number of
application domains where they are likely to interact and
cooperate with humans multiplies. Each of these domains
constitutes an opportunity to develop a more natural and in-
tuitive relationship, working on the robots capacity to detect
social attitudes and adopt expressive stances. While social
robotics has mostly focused on humanoid and zoomorphic
robots, new forms of robots are entering the scene. Robot
swarms are one of them, composed of large numbers of
robots that can evolve in formation and adapt easily to
multiple environments. The robustness of swarm systems
comes mostly from their distributed and scalable control. For
interaction with humans, what makes swarms special is that
they have no defined physicality: they can adopt emerging
configurations depending on environmental constraints, inter-
nal policies and commands issued by a user [1]. This absence
of predictable structure, and the necessity for an observer to
consider multiple individuals, make it necessary to develop
new methods of evaluation to qualify the interaction with
these robots.
Research on the affective reactions to robot swarms has
only started [2]. So far we possess scanty information
about how a swarm’s motion impacts a user’s emotional
response [3]. Specifically, we do not know how the state
Dr. St-Onge and Dr. Beltrame are with the Department of Computer and
Software Engineering, ´
Ecole Polytechnique de Montr´
eal, Qu´
ebec Canada
e-mail: (
Dr. Levillain is with Ensadlab-Reflective Interaction. ´
Ecole Nationale
erieure des Arts D´
ecoratifs, 75240 Paris Cedex 05, France. email:
Dr. Zibetti is with the CHART-LUTIN Laboratory, Universit´
e Paris 8,
93526 Saint Denis Cedex 02, France.
attributed to a swarm (e.g. is it considered as a single entity,
an aggregate of autonomous robots, an ephemeral forma-
tion?) affects its perceived psychological traits (nervous, shy,
aggressive, etc.), as well as the expressivity that may be
attributed to its behaviour. Are the reactions to a robot swarm
similar to those we can feel when observing a school of fish
or a flock of birds? Is a robot swarm able to impress the
sense of a collective movement organized towards a goal?
To what extent an affective relationship can be established
with an ensemble of robots?
This paper preliminarily addresses the fundamental ques-
tions on the cohesion and on the expressivity of a swarm,
and how they are dependent on a defined set of parameters.
In particular we examine how cohesion and expressivity
allow humans to understand the swarm motion dynamics,
and perceive it as a single behavioral entity, as opposed to a
collection of moving objects. These questions are addressed
with a user study on a small swarm of table-top robots. In the
following, we describe related literature, define expressivity,
detail our distributed control mechanism, and how it is
related to expressivity measurements.
This paper relates to two bodies of knowledge that are still
somewhat new in robotics: human-swarm interaction and the
use of non-verbal communication from robots. Both have
some key contributions on which we base this work and the
assumptions used in our study.
A. Human-Swarm Interaction
Human-Swarm Interaction (HSI) differs from common
Human-Robot Interaction (HRI) for the large numbers of
units involved and because it heavily relies on inter-robot
communication. The handful of HSI studies currently pub-
lished focus on specific interface media, and very few
actually study user reaction/perception. In addition, most are
conducted in simulation, suffering from a reality-gap [3].
Podevijn et al. [4] successfully showed by experiments that
the number of robots does not influence the cognitive load
required from a user if the control is performed on the swarm
as a whole.
To convey information about swarm states, a flexible strat-
egy is to use iconic representation that users can recognize
without having to recall them, such as the top LEDs on
each of the robots [5], or make the robots emit machinic
sounds [6]. Note that the latter uses sounds to help the user
be aware of a malfunction in the swarm, not to share high-
level state information.
For broader use, one needs to define the information
conveyed by a swarm, a non trivial task that Cappo et al. [7]
addressed with swarm behaviour descriptors defined as: 1-
action for the global motion of the fleet, 2-goal, i.e. the
destination of the fleet, 3-shape, maintaining a geometry over
the whole motion, 4-heading of the robots, and 5-manner,
i.e. trajectory variations giving various dynamic attributes
to the movement. Over 1000 possible combinations of be-
haviours descriptors were simulated, but no user interaction
study was performed. The shape descriptor is restrictive for
general swarm motion as it removes the possibility of using
distributed path planning algorithms that would not maintain
a shape throughout the complete motion.
Concerning swarm user interfaces, a notable contribution
was the release of the tabletop robots named Zooids [8].
These robots led to a first study of robots group motion
perception (emotional response) [2] and they were used to
examine the perception of abstract robotic displays [9], an
interesting approach for ubiquitous robots. The behaviours
implemented in these studies and the robot control are
centralized, which requires all robots to be connected to a
central node and thus decrease the resilience of the group.
B. Nonverbal communication in robots
Despite the differences between HSI and traditional HRI,
many challenges relative to robots social presence are com-
mon to both fields. As robot swarms are bound to evolve
inside social territories, they need to develop communication
modalities beyond symbols and signs. Nonverbal behaviours,
social attitudes, emotional expressions constitute important
ingredients for a social bond to be established [10]. For
such a connection to be formed and maintained, several
paths have been explored with traditional forms of robotics.
Mimicking the human silhouette and postural structures, a
humanoid robot can express emotional states using a combi-
nation of body posture and facial expressions [11]. Yet more
abstract, high-level motion patterns can contribute to the
emotional expression, without requiring a human appearance,
or even specific emotions to be expressed. For instance,
the kinematics of movement have been shown to participate
in the emotional appraisal of an action [12], [13]. Motion
characteristics such as path curvature and acceleration are
correlated with different levels of perceived arousal and
valence [14], [15]. Yet, one of the HSI current challenges
relating to human-swarm non-verbal communication, is the
state estimation and visualization of swarms [16]. Besides the
aspect of designing appropriate algorithms, a very important
issue is whether humans may be able to understand swarm
motion dynamics [17] and consequently emotionally react
to it. To consider the swarm’s specificity with respect to
nonverbal communication, one needs to take into account
the distributed nature of such an entity, and thus develop
the adequate concepts to determine how socially impactful a
swarm can be. This is what we are considering in this paper,
evoking the notion of swarm expressivity.
This work uses the level of expressivity of a swarm of
robots as a behavioral metric. To ensure the context of this
study is well understood, we first need to define expressivity
and explain its relationship with robot control.
A. Expressivity and Coherence
A common denominator for the different modalities of
social presence is the notion of expressivity. An expressive
behaviour can be considered one that successfully transmits
a particular emotion, an attitude, or a general disposition
to act and react in certain ways. Phrased by Simmons &
Knight [18], expressivity represents the ability to “convey
an agent’s attitude towards its task or environment”. The ex-
pressivity of a movement determines how natural, readable,
or easily understandable this movement may appear. Thus,
expressivity determines to a great extent the capability for an
intuitive and transparent interaction with a robot, including
the interaction with a robot swarm.
Because of the distributed nature of robot swarms, the
notion of expressivity is bound to take a different meaning
from traditional approaches that connect expressivity to ges-
tural and morphological properties. A swarm has no body nor
body parts to express feelings or attitudes. Without a definite
physicality, a swarm can reconfigure and adapt to different
environments and commands coming from the user. In this
context, an observer has to consider the emergent properties
resulting from multiple individual behaviours, for instance
the tendency for the individuals to remain close to each
other, or to adopt similar velocities. Determining a swarm’s
expressivity is therefore a different process than considering
the movements of a single robot, or even of a small group of
centralized robots. Instead of relying on body and biological
motion perception, assessing the behaviour of a swarm
depends on specific computations that rely on ensemble
perception, that is the representation of a collection of objects
as a single entity, and to attach perceived properties, such as
mean orientation or velocity, to this entity [19].
The literature on swarm behaviour often distinguishes
two different parameters that govern the representation of
a swarm as a coherent entity [20], [21]: a parameter of
cohesion that represents a tendency for individuals to remain
close to each other, and a parameter of synchronization. The
synchronization can be in terms of velocity or alignment. For
the purpose of this article, we make a distinction between
three parameters (Table I): a parameter of aggregation,
corresponding to the impression for an observer that the
robots forming the swarm tend to stay together rather than
scattering; a parameter of synchronization, or the impression
that the robots are aligning their movements; and a parameter
of leadership addressing the impression that the robots are
following or chasing a member of the swarm. More specif-
ically, we will use the term “cohesion” to refer to a global
property resulting from the sum of the three aforementioned
parameters and to construct a representation of the robot
aggregation the tendency to perceive the robots as re-
maining close to each other
synchronization the tendency to perceive the robots as syn-
chronizing their movements
leadership the tendency to perceive the robots as fol-
lowing one of theirs
B. Swarm bio-inspired control
Many biological societies which consist of relatively sim-
ple members (creatures) can collectively perform complex,
meaningful, and intelligent tasks. Among the most popu-
lar formalization of biological swarm behaviours, potential
functions are a simple, yet flexible control approach. Ar-
tificial potential functions have been used extensively for
robot navigation and control, for instance in [22] and [23].
In Brambilla’s swarm taxonomy [24], the potential-based
methods are part of the virtual physics-based designs. These
methods assume that the robots are able to perceive and
distinguish neighboring robots and obstacles, and to estimate
their distance and relative position. Each robot then computes
a virtual force vector:
, where θiand diare the direction and the distance of
the ith perceived obstacle or robot and the function fi(di)
is derived from an artificial potential function. One of the
most commonly used artificial potentials is the Lennard-
Jones potential, adapted for our physical system as shown
in Fig. 1.
Averaging potential force algorithms such as Lennard-
Jones are referred as the flocking behaviour. The two parts
of the potential equation represent the attractor and repulsor
effect. This potential is driven by two parameters: the target
distance for the system to be stable and the gain of
the potential. While the equation itself requires only basic
arithmetic operations and a few parameters, it is based on
the assumptions that each robot has communication with its
neighbors and their relative position.
Based on its popularity, we selected Lennard-Jones po-
tential, as derived in Fig. 1, to control the behaviour of
our robotic swarm. In this control approach, a goal (target
location) is represented as an attractor influencing the whole
C. Control attributes
A simple behaviour in mobile swarm systems, such as
flocking, often leads to emergent states [1]. Without formal
analytical model of the robots in their ecosystem, the control
parameters must be optimized from simulations. One can
then observe and deconstruct the transient states generated by
a given simple set of control parameters. In this work, instead
of directly mapping the control parameters to coherence and
0 10 20 30 40 50 60 70 80
- +
LJ =Pn
D[( t
Lennard-Jones Potential
Fig. 1. Lennard-Jones potential adapted for wheeled robots formation. The
’-’ and ’+’ domains are respectively the repulsive and attractive parts, for
which the pivot point is set with parameter t.Dis the distance between
two robots and a parameter acting as a control gain on the potential.
expressivity, we designed a set of higher level motion control
1) the average inter-robot distance,
2) the spatial synchronicity of the swarm, i.e. the robots
move as a cohesive group, and
3) the temporal synchronicity of the swarm, i.e. the robots
move simultaneously.
Each attribute is positioned on a continuous range
(close/far, synchronized/unsynchronized), by the behaviour
control parameters. Indeed, increasing the distance (target)
parameter alone in a Lennard-Jones potential lead to an
unstable and unpredictable inter-robot distance over time.
Therefore, the epsilon/target pair has to be manipulated to-
gether to get a stable formation for each inter-robot distance.
Leveraging the non-stable spectrum of the range of these two
parameters, one can also influence the spatial synchronicity
of the group. In other words, the more unstable a given pair
of parameters is, the more sparse the robot motion will be.
Temporal synchronicity requires the use of another control
parameter in the potential definition: the delay or latency for
each robot in registering a goal attractor. By delaying the
influence of a goal’s attraction on certain robots, we influence
the temporal synchronicity. For instance, a leader robot can
notice the goal attractor seconds before the rest of the swarm,
thus creating a break in the temporal synchronicity.
D. Hypothesis
The three motion control attributes we just described gave
us the possibility to assess to which extent the expressivity
attributed to the swarm depends on certain emergent states.
Given the necessity to consider multiple individual robots,
we surmise that an observer has to represent the swarm
as a single entity before attributing any kind of properties
to its behaviour. We can suppose therefore that a certain
level of perceived cohesion is necessary for expressivity to
develop, and we should expect to observe a relationship
between the perceived cohesion, as measured by parameters
of aggregation and organization, and a score of expressivity
attributed to the swarm.
We make the following hypothesis:
H1 Considering the swarm as a coherent and stable entity
should depend on the possibility to identify moments
of aggregation and organization in the swarms move-
H2 Expressivity should also be related to the parameters of
aggregation, synchronization and leadership, inasmuch
as a sufficient level of cohesion is necessary for the
swarm to be considered as a single entity. However,
excessive cohesion may be detrimental to the overall
expressivity if it results in stereotyped motion patterns.
To enhance our knowledge on the relationships between
coherence and expressivity in a robot swarm, we conducted
a user study focused on the validation of the previously
mentioned hypotheses.
A. Methods
We recruited 27 participants with good knowledge and
experience of dance. For this study on swarm motion per-
ception, we intentionally targeted this specific background
to give us sensible insights on the slight differences in each
of the swarm motion states. Dancers and choreographers are
S+ robots tend to remain close together
S- robots tend to scatter
Temporal Syn-
T+ robots tend to move simultaneously
T- robots tend to follow a leader
Inter-robot dis-
D+ robots are moving with large distances be-
tween them
D- robots are moving with small distances be-
tween them
the experts of body motion, let it be human or artificial.
We believe the conclusions obtained from their answers can
better help us define the motion parameters for a broader
spectrum of users. From the 27 participants, 4 are men, 22
women and 1 selected ’other’; two thirds are dance students
(19), while the others are freelancers (8). The participants
did not receive any kind of financial compensation for the
study: they participated out of curiosity for natural interaction
with robotic systems. The study protocol was approved by
the Paris 8 University research board and Polytechnique
de Montr´
eal’s ethical committee. Participants signed an in-
formed consent form to partake in the study.
To illustrate the different motion patterns in multiple
sequential studies, we alternated between two sets of six
Zooids robots. The Zooids are small table-top robots of 2.6
cm diameter, localized from structured light emitted by a
ceiling projector [8]. While our behavioural scripts can be
ported on any hardware platform, we selected the Zooids
for the minimal setup time and ease of transportation. The
Zooids were programmed to obey to pre-calibrated motion
scripts. While the exact path of each robot is not determined,
Fig. 2. Six Zooids moving toward the user to form a figure. The green
letters show the successive goals covered by each motion sequence.
1 on a scale from 0 to 6, indicate to which extent you agree with the
following statements:
the robots tend to stay in groups
the robots tend to synchronize their movements
the robots tend to follow one of theirs
2 on a scale from 0 to 6, indicate to which extent you agree with the
following statement: the robots form a coherent and stable group
and seem to progress while connected to each other
3 on a scale from 0 to 6, how would you evaluate the expressivity
of the robot swarm?
the group motion parameters and goal locations are scripted.
As shown in Table II, the three high level motion attributes
described earlier where used as binary inputs, generating 8
possible combinations, i.e. 8 different motion scripts. Each
motion followed the same goal sequence (see Fig. 2): (1)
from point A to point B, (2) from point B to point C, (3)
from C to B, (4) from B to C, and (5) from C to A.
Participants were asked to sit in front of the table on which
the Zooids performed. They had a 14 questions to answer on
a tablet (available in French and English) after observing
each sequence. Sequences were shown only once to the
participants, but they were played following one of three
possible orders: 1-2-3-4-5-6-7-8, 5-6-7-8-1-2-3-4 and 1-2-7-
8-3-4-5-6. The motion sequences were triggered one at the
time by the experimenters when the participant confirmed all
questions were answered. The experimenter also explained
beforehand that an unknown number of motion sequences
going through the same goals would be automatically gen-
erated with different motion attributes.
To assess the values of coherence and expressivity at-
tributed to the swarm, participants completed a survey com-
prising three different scales (see table III) : (i) a scale eval-
uating the organization perceived in the swarm’s behaviour;
(ii) a scale measuring the cohesion attributed to the swarm
(i.e. whether it is considered as a coherent and stable entity)
; and (iii) a scale assessing the level of expressivity of the
swarm’s behaviour. For each item of the different scales, we
used a seven-point Likert scale with response ranging from
0 (strongly disagree) to 6 (strongly agree).
Fig. 3. Average scores of expressivity for the different conditions of
spatial synchronicity (S+/S-), temporal synchronicity (T+/T-), and inter-
robot distance (D+/D-).
B. Results
This study presents a large number of tied ranks for a rel-
atively small dataset (27 participants). We used Kendall’s τb
correlation test to assess the contribution of each parameters
in our dataset. We extract the perceived organization from
the measures of cohesion and expressivity. As we could not
assume that the psychological distance between the scores of
expressivity and between those of cohesion were equivalent,
we used an ordinal logistic regression to examine the effect
of spatial synchronization, temporal synchronization, and
distance on both perceived coherence and expressivity.
How do the parameters of perceived organization con-
tribute to the cohesion attributed to the swarm?
We found a positive correlation between cohesion and
tendency to stay in groups: a higher perceived tendency for
the robots to stay in groups is more likely to be associated
with a higher perceived cohesion (τb=.398,p < .001).
Similarly, we found that a higher perceived tendency for
the robots to synchronize their movements is more likely to
be associated with a higher perceived cohesion (τb=.440,
p<.001). Finally, we found a significant positive association
between the perceived tendency for the robots to follow one
of theirs and the cohesion attributed to the swarm (τb=.309,
How do the parameters of perceived organization con-
tribute to the expressivity of the swarm?
The correlation scores were less important than for the
cohesion, but we still found a positive significant association
between expressivity and the perceived tendency for the
robots to stay in groups (τb=.148,p=.008), as well as
the perceived tendency for the robots to follow one of theirs
(τb=.197,p<.001). While the tendency to stay in groups
and the possibility to perceive chasing relationships between
the robots seem to benefit the expressivity of the swarm,
an excessive level of synchronization may be detrimental
to this measure, as indicated by the absence of significant
Fig. 4. Average scores of cohesion for the different conditions of
spatial synchronicity (S+/S-), temporal synchronicity (T+/T-), and inter-
robot distance (D+/D-).
association between the perceived tendency for the robots to
synchronize their movements and expressivity (τb=.033,
How the synchronization and distance between the robots
affect the expressivity and cohesiveness of the swarm?
Temporal synchronization had a significant effect on the
expressivity of the swarm (see Fig. 3). With temporally
asynchronous conditions, expressivity was 1.647 (95% CI,
1.012 to 2.681) times more likely to increase (χ2(1) =
4.028,p=.045). However, we did not find an impact of
spatial synchronization: the odds of spatially asynchronous
conditions to be considered expressive was similar to that of
spatially synchronous conditions (odds ratio of 0,698, 95%
CI, 0.430 to 1.134), χ2(1) = 2.107,p=.147. Similarly, the
odds of large spacing conditions to be considered expressive
did not differ from that of small spacing conditions (odds
ratio of 1,389, 95%CI, 0.855 to 2.256), χ2(1) = 1.761,
Compared to expressivity, the cohesion score was affected
principally by spatial synchronization (see Fig. 4): with
spatially asynchronous conditions, cohesion was 0.321 (95%
CI, 0.194 to 0.530) times more likely to decrease (χ2(1) =
19.725,p < .001). Temporal synchronization and distance
did not affect significantly the score of cohesion: temporal
synchronization (odds ratio of 0,661, 95% CI, 0.407 to 1.075;
χ2(1) = 2.787,p=.095); distance (odds ratio of 0,859, 95%
CI, 0.530 to 1.392; χ2(1) = 0.381,p=.537).
C. Discussion
In this study on the perception of swarm behavior, we
assumed that the expressivity of a swarm is dependent on a
sense of coherence emanating from the robots movements,
itself contingent upon parameters of aggregation, synchro-
nization and leadership. We verified the hypothesis that the
score of cohesion (measuring to what extent people consider
the swarm as a coherent and stable entity) is linked to
the possibility of identifying moments of aggregation and
organization. We found indeed that all the three parameters
we measured (tendency for the robots to stay in groups, to
synchronize their movements, and to follow one of theirs)
were positively associated with the score of cohesion. As
predicted, the relationship between those parameters and
expressivity is slightly more complicated. Motion patterns
considered expressive are more likely to be associated with
a higher level of aggregation and with the impression that
the robots were following one of them, but we did not
find a significant correlation with the score of synchro-
nization. We also found that conditions more favorable to
expressivity are those in which movements are temporally
asynchronous, confirming the idea that a high synchronicity
may be detrimental to expressive patterns. It is interesting to
observe that, contrary to the score of expressivity, the score
of cohesion is mainly affected by spatial synchronization,
with spatially asynchronous conditions being considered less
cohesive. We observe an interesting relationship between
the two parameters: we postulate that a sufficient level of
cohesion is necessary for the swarm to be considered expres-
sive (hence the positive correlation between expressivity and
the aggregation and organization parameters), but coherence
and expressivity dissociate with respect to the impact of
temporal synchronization (detrimental to expressivity) and
spatial asynchrony (detrimental to cohesion).
In this work, we presented the preliminary results of a user
study focused on the perception of swarm behaviours. The
expressivity and coherence of the robot group was expressed
in term of high level control attributes, injected as control
parameters of a common distributed behaviour: flocking.
The results show that the perceived cohesion of the group
increases with their tendency to stay in groups (be organized)
and their spatial synchronicity. The expressivity of the swarm
was also increased by their tendency to stay in groups, but
was reduced by the temporal synchronicity. Somehow our
contribution is the prerequisite to further address the question
of the “establishment of a possible affective relationship with
the swarm as a behavioral entity” [25]. We believe these
preliminary results represent a stepping stone on the path to
a better understanding of artificial swarm perception.
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... As we observed in a previous study [3], with robot swarms, the necessity to consider multiple individuals makes it necessary to develop intuitive channels of communication that rely on collective movements. We therefore examine a swarm's nonverbal behaviour as a possible communication way to help increase SA specifically in exploration scenarios. ...
... Recently, researchers have started examining how swarm behaviours are associated with emotional states [24]. Our own experimental investigation has demonstrated how the expressiveness of a swarm is connected with the variation of parameters of spatial and temporal synchronicity [3]. ...
... The first study is an attempt to establish a typology of communicative intents conveyed by the collective movements of a robot swarm. This is a follow-up to our previous studies [3] that examined the level of perceived organization and the state attributed to some collective patterns of movement. Collective behaviours of a robot swarm may convey messages for a user to get information about the swarms state and current operations. ...
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The control of multiple robots in the context of tele-exploration tasks is often attentionally taxing, resulting in a loss of situational awareness for operators. Unmanned aerial vehicle swarms require significantly more multitasking than controlling a plane, thus making it necessary to devise intuitive feedback sources and control methods for these robots. The purpose of this article is to examine a swarm's nonverbal behaviour as a possible way to increase situational awareness and reduce the operators cognitive load by soliciting intuitions about the swarm's behaviour. To progress on the definition of a database of nonverbal expressions for robot swarms, we first define categories of communicative intents based on spontaneous descriptions of common swarm behaviours. The obtained typology confirms that the first two levels (as defined by Endsley: elements of environment and comprehension of the situation) can be shared through swarms motion-based communication. We then investigate group motion parameters potentially connected to these communicative intents. Results are that synchronized movement and tendency to form figures help convey meaningful information to the operator. We then discuss how this can be applied to realistic scenarios for the intuitive command of remote robotic teams.
... Levillain et al. expands on this topic by finding "distance" and "organized movement" plays a role in "perceived expressivity" of the robotic swarms. [16]. Our investigation will use the Godspeed questionnaire with modifications based on the other research cited here. ...
... The data collected from the experiments included drawings and a post-experiment questionnaire that queried the participant on their experience for each formation. The questionnaire included questions from the Godspeed questionnaire [5], which is a set of questions to evaluate the participant's perception of social interactions with a robot using the Likert scale [16]. This questionnaire is used to measure anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of the drone swarms. ...
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Human-to-human communications are enriched with affects and emotions, conveyed, and perceived through both verbal and nonverbal communication. It is our thesis that drone swarms can be used to communicate information enriched with effects via nonverbal channels: guiding, generally interacting with, or warning a human audience via their pattern of motions or behavior. And furthermore that this approach has unique advantages such as flexibility and mobility over other forms of user interface. In this paper, we present a user study to understand how human participants perceived and interpreted swarm behaviors of micro-drone Crazyflie quadcopters flying three different flight formations to bridge the psychological gap between front-end technologies (drones) and the human observers' emotional perceptions. We ask the question whether a human observer would in fact consider a swarm of drones in their immediate vicinity to be nonthreatening enough to be a vehicle for communication, and whether a human would intuit some communication from the swarm behavior, despite the lack of verbal or written language. Our results show that there is statistically significant support for the thesis that a human participant is open to interpreting the motion of drones as having intent and to potentially interpret their motion as communication. This supports the potential use of drone swarms as a communication resource, emergency guidance situations, policing of public events, tour guidance, etc.
... The intersection of robots and arts has become an active object of study as both researchers and artists push the boundaries of the traditional conceptions of different forms of art by making robotic agents dance (Nakazawa et al., 2002;LaViers et al., 2014;Bi et al., 2018), create music (Hoffman and Weinberg, 2010), support stage performances (Ackerman, 2014), create paintings (Lindemeier et al., 2013;Tresset and Leymarie, 2013), or become art exhibits by themselves (Dean et al., 2008;Dunstan et al., 2016;Jochum and Goldberg, 2016;Vlachos et al., 2018). On a smaller scale, the artistic possibilities of robotic swarms have also been explored in the context of choreographed movements to music (Ackerman, 2014;Alonso-Mora et al., 2014;Schoellig et al., 2014), emotionally expressive motions (Dietz et al., 2017;Levillain et al., 2018;St.-Onge et al., 2019;Santos and Egerstedt, 2020), or interactive music generation based on the interactions between agents (Albin et al., 2012), among others. ...
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In this paper, we present a robotic painting system whereby a team of mobile robots equipped with different color paints create pictorial compositions by leaving trails of color as they move throughout a canvas. We envision this system to be used by an external user who can control the concentration of different colors over the painting by specifying density maps associated with the desired colors over the painting domain, which may vary over time. The robots distribute themselves according to such color densities by means of a heterogeneous distributed coverage control paradigm, whereby only those robots equipped with the appropriate paint will track the corresponding color density function. The painting composition therefore arises as the integration of the motion trajectories of the robots, which lay paint as they move throughout the canvas tracking the color density functions. The proposed interactive painting system is evaluated on a team of mobile robots. Different experimental setups in terms of paint capabilities given to the robots highlight the effects and benefits of considering heterogeneous teams when the painting resources are limited.
... However, for faceless robots or robots with limited degrees of freedom for which mimicking human movement is not an option, creating expressive behaviors can pose increased difficulty [8,23,52]. We are interested in exploring the expressive capabilities of a swarm of miniature mobile robots, for which the study of expressive interactions is sparse [17,36,55]. This can be contrasted with more anthropomorphic robots, for which there is already a preconceived understanding of emotive expressiveness. ...
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This paper explores the expressive capabilities of a swarm of miniature mobile robots within the context of inter-robot interactions and their mapping to the so-called fundamental emotions. In particular, we investigate how motion asnd shape descriptors that are psychologically associated with different emotions can be incorporated into different swarm behaviors for the purpose of artistic expositions. Based on these characterizations from social psychology, a set of swarm behaviors is created, where each behavior corresponds to a fundamental emotion. The effectiveness of these behaviors is evaluated in a survey in which the participants are asked to associate different swarm behaviors with the fundamental emotions. The results of the survey show that most of the research participants assigned to each video the emotion intended to be portrayed by design. These results confirm that abstract descriptors associated with the different fundamental emotions in social psychology provide useful motion characterizations that can be effectively transformed into expressive behaviors for a swarm of simple ground mobile robots.
... Expressive variations in general movement patterns are also apt to reveal information about the characteristics of the messenger, that is to reveal idiosyncratic information [2]. An expressive movement can be considered one that transmits a particular emotion, an attitude, or a general disposition to act and react in certain ways [3,4]. Studies investigating the expressivity of behaviour in relation to idiosyncratic information typically look to identify the combinations of gestures and postures, as well as behavioural patterns, that convey a specific emotion or attitude [5,6,7]. ...
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We tackle the issue of expressive movement in non-humanlike robots, conducting a study with the goal of providing a characterization of expressive qualities embedded in the movements of a simple robot. We provide evidence that expressivity can be considered as a distinct modality of evaluation, distinct from other ways to consider a movement. Our first results indicate that expressivity is primarily associated to movements possessing a form of granularity and readability.
Multi-robot systems (MRS) involve multiple individual robots working together towards a common goal. Human multi-robot interaction research has focused on either using a visual interface for humans or having the human use speech or gestures to interact with multiple agents. Some difficulties lie with the humans’ ability to collect information from multiple robots. This paper investigates the use of MRS to visually communicate with vehicle drivers for roadway safety information by depicting various shapes and symbols. The robots will move to visually display an internationally recognizable shape or alphanumeric message for the human to interpret. These messages could be quickly deployed along the roadway to inform operators of drastic changes in the road conditions such as crashes, environmental hazards, or construction zones. The quick deployment offers a suitable addition to fixed roadway signs which are unable to rapidly adapt to changing conditions. A computer-generated display conveys these symbols to human test subjects using multiple agents. The chosen statistical analyses performed include Analysis of Variance (ANOVA) with additional Tukey’s Honestly Significant Difference (HSD) and Chi-square tests. Researchers used 25 participants in the tests conducted through Zoom where 62% of responses correctly identified the shapes with an average time of 10.84 seconds. The tests demonstrated a statistically significant average estimation time difference among the various shapes, and that the participants can accurately identify each shape. There was also an association between accuracy of prediction and number of robots used in the trials.
Self-assembly of shapes using robot swarms has applications spanning architecture, functional materials, and art. Typically, the amount of robotic material needed to represent the shape is relative to the shape's area, with robots filling in the whole shape. To save in robotic material, we explore the ability to automatically aggregate on the frontier of shapes, essentially forming outlines, using a self-organised decentralised mechanism. An image is projected on top of a swarm of robots, and the robots sense the light colour and communicate this information to their neighbours to detect salient features. Results in simulation show the swarm can detect salient features, draw with different line thicknesses, adapt to changes in the feature over time, and scale to 300 robots in reality and up to 1000 robots in simulation. We then show a dynamic performance where robots continuously reconfigure in simulation to form a video.
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In this paper, we demonstrate how behavioural structure, such as a finite state machine, can emerge in minimal robots without computation nor memory capabilities. As a case study we observe the ability of a group of non-holonomic robots to form robust, self-healing circle formations in a decentralized manner using only a limited frontal binary sensor. We present a grid-search method to find suitable parameters that promote the formation of a stable circle. We then examine how the parameters of the controllers affect the appearance of the behaviour, and provide theoretical proof for its emergence and self-healing properties. We validate the proposed model through a set of experiments with ten mobile real robots. Our results with real robots match the simulated experiments and provide insights on how a simple, computation-free behaviour can generate complex spatio-temporal dynamics.
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As robots increasingly enter our everyday life, we envision a future in which robots are ubiquitous and interact with both ourselves and our environments. This paper introduces the concept of ubiquitous robotic interfaces (URIs), multi-robot interfaces capable of mobility, manipulation, sensing, display and interaction. URIs interact directly with the user and indirectly through surrounding objects. A key aspect of URIs is their ability to display information to users either by collectively forming shapes or through their movements. In this paper, we focus on the use of URIs to display information in ubiquitous settings. We first investigate the use of abstract motion as a display for URIs by studying human perception of abstract multi-robot motion. With ten small robots, we produced 42 videos of bio-inspired abstract motion by varying three parameters (7 x 2 x 3): bio-inspired behavior, speed and smoothness. In a crowdsourced between-subjects study, 1067 subjects were recruited to watch the videos and describe their perception through Likert scales and free text. Study results suggest that different bio-inspired behaviors elicit significantly different responses in arousal, dominance, hedonic and pragmatic qualities, animacy, urgency and willingness to attend. On the other hand, speed significantly affects valence, arousal, hedonic quality, urgency and animacy while smoothness affects hedonic quality, animacy, attractivity and likeability. We discuss how these results inform URI designers to formulate appropriate motion for different interaction scenarios and use these results to derive our own example applications using our URI platform, UbiSwarm.
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A new race of artifacts comes equipped with behavioral properties. Those properties transmute the very nature of the object, granting it a life of its own and a special status that stems from the psychological attributions humans naturally produce when confronted by autonomous movements. This article examines what makes behavioral objects special in terms of the psychological properties they evoke in an observer. We look into the notion of behavior and evaluate to what extent the concept of anthropomorphism is a valid construct when considering the behavior of artificial objects. Based on recent research in cognitive psychology, we propose a framework to conceptualize the way people infer psychological attributes from movement, and the way it applies to behavioral objects.
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This paper introduces swarm user interfaces, a new class of human-computer interfaces comprised of many autonomous robots that handle both display and interaction. We describe the design of Zooids, an open-source open-hardware platform for developing tabletop swarm interfaces. The platform consists of a collection of custom-designed wheeled micro robots each 2.6 cm in diameter, a radio base-station, a high-speed DLP structured light projector for optical tracking, and a software framework for application development and control. We illustrate the potential of tabletop swarm user interfaces through a set of application scenarios developed with Zooids, and discuss general design considerations unique to swarm user interfaces.
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We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse the psychophysiological state (stress, anxiety, happiness) of our participants. Our results show that the number of robots to which a human is exposed has a significant impact on the psychophysiological state of the human and that higher numbers of robots provoke a stronger response.
We study the reality-gap effect (the effect of the inherent discrepancy between simulation and reality) on the human psychophysiological state, workload and reaction time in the context of a human-swarm interaction scenario. In our experiments, 37 participants perform a supervision task (i.e., the participants have to respond to visual stimuli produced by a robot swarm) with a real robot swarm and with simulated robot swarms. Our results show that the reality-gap significantly affects the human psychophysiological state, workload and reaction time.
We propose and evaluate a multirobot system designed to enable live theatric presentation of episodic stories through online interaction between a performer and a robot system. The proposed system translates theatric performer intent into dynamically feasible robot trajectories without requiring prior knowledge of the ordering or timing of the desired robot motions. The system enables a user to issue instructions composed of desired motion descriptors at arbitrary times to specify the motion of the robot ensemble. The system refines user motion specifications into safe and dynamically feasible trajectories thereby reducing the cognitive burden placed on the performer. We evaluate the system on a team of aerial robots (quadrotors), and show through offline simulation and online performance that the proposed system formulation translates online input into non-colliding dynamically feasible trajectories enabling the performance of an epic poem over the course of a three act performance spanning fifteen minutes of coordinated flight by a six robot team.
Conference Paper
As robots become ubiquitous in our everyday environment, we start seeing them used in groups, rather than individually, to complete tasks. We present a study aimed to understand how different movement patterns impact humans' perceptions of groups of small tabletop robots. To understand this, we focus on the effects of changing the robots' speed, smoothness, and synchronization, on perceived valence, arousal, and dominance. We find that speed had the strongest correlation to these factors. With regard to human emotional response to the robots, we align with and build on prior work dealing with individual robots that correlates speed to valence and smoothness to arousal. In addition, participants noted an increase in positive affect in response to synchronized motion, though synchronization had no significant impact on measured perception. Based on our quantitative and qualitative results, we describe design implications for swarm robot motion.
Due to the recent technological progress, Human-Robot Interaction (HRI) has become a major field of research in both engineering and artistic realms, particularly so in the last decade. The mainstream interests are, however, extremely diverse: challenges are continuously shifting, the evolution of robot’ skills, as well as the advances in methods for understanding their environment radically impact the design and implementation of research prototypes. When directly deployed in a public installation or artistic performances, robots help foster the next level of understanding in HRI. To this effect, this paper presents a successful interdisciplinary art-science-technology project, the Aerostabiles, leading to a new way of conducting HRI research. The project consists of developing a mechatronic, intelligent platform embodied in multiple geometric blimps—cubes—that hover and move in the air. The artistic context of this project required a number of advances in engineering on the aspects of localization and control systems, flight dynamics, as well as interaction strategies, and their evolution through periods of collective activities called “research-creation residencies”. These events involve artists, engineers, and performers working in close collaboration, sometimes, over several weeks at a time. They generate fruitful exchanges between all researchers, but most of all, they present a unique and creative way to direct and focus the robotics development. This paper represents an overview of the technical contributions from a range of expertise through the artistic drive of the Aerostabiles project.