CHI 2018 Late-Breaking Abstract

CHI 2018, April 21–26, 2018, Montréal, QC, Canada

Effect of Expressive Lights on Human
Perception and Interpretation of
Functional Robot
Sichao Song
Seiji Yamda
The Graduate University for
National Institute of Informatics
Advanced Studies (SOKENDAI), and SOKENDAI
Department of Informatics
2-1-2 Hitotsubashi, Chiyoda,
2-1-2 Hitotsubashi, Chiyoda,
Tokyo, JapanTokyo, Japanseiji@nii.ac.jp
sichaos@nii.ac.jp

Abstract
Because appearance-constrained robots lack expressiveness, human users often find it hard to understand their
behavior and intentions. To address this, expressive lights
are considered to be an effective means for such robots tocommunicate with people. However, existing studies mainly
focus on specific tasks or goals, leaving the knowledge of
how expressive lights affect people’s perception still unknown. In this pilot study, we investigate such a question
by using a Roomba robot. We designed two light expressions, namely, green and low-intensity (GL) and red andhigh-intensity (RH). We used open-ended questions to evaluate people’s perception and interpretation of the robot,
which showed different light expressions as a way to communicate. Our findings reveal that simple light expressions
can allow people to construct rich and complex interpretations of a robot’s behavior, and such interpretations areheavily biased by the design of expressive lights.

Author Keywords
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For all other uses, contact the owner/author(s). Copyright is held by theauthor/owner(s).
CHI’18 Extended Abstracts, April 21–26, 2018, Montréal, QC, Canada.
ACM ISBN 978-1-4503-5621-3/18/04.
https://doi.org/10.1145/3170427.3188547

Expressive lights; color psychology; appearance-constrainedrobot; human-robot interaction (HRI)ACM Classification Keywords
H.5.m. [Information Interfaces and Presentation (e.g., HCI)]:
User Interfaces - Theory and methods

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Introduction
People are explanatory creatures. Due to our tendency toform explanations of things, we build mental models, ourconceptual models of the way objects work or people behave, of those things and use the models to help us understand our experiences and handle unexpected occurrences [13]. Therefore, we naturally adapt our social skills
and exhibit similar social behavior when we first meet ahuman-shaped robot. However, a large number of robots
currently in use for applications such as search and rescue and domestic cleaning are neither anthropomorphic norzoomorphic. When we first encounter such robots, the lackof appropriate mental models and knowledge with regard tothese robots can lead to unsmooth or even failed interaction[11]. Therefore, there is a significant challenge in finding
effective ways for these robots to successfully interact withhuman users.
Existing approaches mainly focus on motion cues [12, 4, 3,14]. Unfortunately, such methods suffer from low expressibility and are hard, if not impossible, to apply in many application scenarios such as places that have restricted space.
Alternatively, previous research [2, 15, 5] showed that expressive lights, as a dynamic vision cue, can be used for
robots to communicate with humans. Basically, expressive
lights have been shown to be effective in various humanrobot interaction (HRI) contexts such as indicating internal
states [2], communicating intent [15], and expressing emotion [5]. In general, a commonality with regard to most ofthese studies is that they focused on specific tasks or goals.
Therefore, it is still unknown how expressive lights affectpeople’s interpretation of a robot in general.
In this work, we investigate this research question. On thebasis of color psychology theories and related literature, we
designed two light expressions, namely, GL (green and low-

intensity) and RH (red and high-intensity). We designed anexperiment to study people’s perception and interpretationof a Roomba robot’s behavior in different expressive lights
conditions and employed open-ended questions for evaluation. Findings from this pilot study reveal that simple light
expressions can allow people to construct rich and complex
interpretations of a robot’s behavior, and such interpretations are heavily biased by the design of expressive lights.
This is supported by Joseph’s claim [10] that “...single bit ofcommunication can leverage an enormous amount of social, cultural and emotional capital, giving it a significancefar greater than its bandwidth would seem to suggest."Expressive Light Design
Color psychologists have intensively investigated various
aspects of color, including color vision, color symbolism andassociation, and color effects on psychological and biological functioning [7]. Particularly, red has been shown to be a
critical color and has thus garnered the majority of researchattention. Red is the color of blood, and dynamic variations
in visible blood flow on the face and body can indicate fear,
arousal, anger, and aggression [1]. Red is also a term thatappears in almost all lexicons and, moreover, in many sayings such as “in the red." Besides red, the color green has
been intensively studied as well. It has positive links in thenatural realm, for example, green foliage and vegetation [9].
Studies on hue color associations suggest that color meanings can be grounded in two basic sources: learned associations that develop from repeated pairings of colors
with particular concepts or experiences and biologically
based proclivities to respond to particular colors in particular ways in particular situations [8]. For instance, a specificred-danger association can be generated from experienceswith regard to (life-threatening) situations such as viewing
blood, an angry face, traffic lights, and/or warning signals

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Battery bankArduino Uno R3

NeoPixel LED strip

Figure 1: Configuration of Roomba
robot with LED lighting system

Figure 2: NeoPixel LED strip (60pixels)CHI 2018, April 21–26, 2018, Montréal, QC, Canada

and sirens [9]. Similarly, green can be associated with positive meanings, e.g., approach and pleasure, due to experiences with green traffic lights and the general image ofbeing the color of nature.
On the basis of the above survey on color psychology, we
decided to focus on two colors: green and red. They produce opposite effects on human psychological functioning.
Basically, green can be associated with weak and positive
affect and induce approach-like behaviors in a human. In
comparison, red can be associated with strong and negative affect and induce avoidance-like behaviors. Besides
color, two more parameters, waveform and intensity (frequency), needed to be decided on to design expressive
light patterns. In particular, Terada et al. [16] studied colorand dynamic parameters for representing emotions. They
found that a rectangular waveform with a high frequency
represents intense emotions, while a sinusoidal waveformwith a low frequency represents weak emotions. On thebasis of their work, we decided to combine a sinusoidal
waveform and a low frequency with green to enhance theeffect of the color green. Similarly, we combined a rectangular waveform and a high frequency with red to enhancethe effect of the color red.

Pilot Study
We conducted an experiment in which we installed an LED
lighting system on an iRobot Create 2 robot, which is a
Roomba robot. Roomba is a series of autonomous robotic
vacuum cleaners used in indoor environments. It perfectly
fits the definition of an appearance-constrained robot andhas very limited ways to express itself, e.g., moving forward/backward and spinning. Figure 1 shows the configuration of the robot with LED lighting. We used one meter ofa NeoPixel LED strip (60 pixels, Figure 2). The LED strip
was controlled by an Arduino Uno R3 board, and both thestrip and the board were powered by a 5-V, 3-A portable
powerbank. The same board was also used to control the
movements of the robot.
By using expressive lights, we are effectively enabling arobot to modify its appearance as a method of communicating with humans and, moreover, providing additional cues
to assist humans in interpreting the robot’s true intentions.
We expect that the simple light expressions we designed
can allow people to construct rich and complex interpretations of the robot’s behavior and that such interpretations
will be heavily biased by the design of the expressive lights(GL or RH).
Procedure
We designed two similar practical HRI contexts, corridor
and corner, which are common for indoor autonomous
robots [2]. Specifically, the Roomba robot moved along anarrow corridor (corridor context) or approached a corner ofthe corridor (corner scenario). In the two cases, the robot
encountered a person and stopped before it ran into theperson. While stopped, the robot further showed GL, RH,
or simply no lights (see Figure 3). We presumed that the
robot’s intention, i.e., what it was doing, in such contexts
would be ambiguous and thus could be interpreted in various ways. We presumed that the added expressive lights
could significantly affect and bias people’s perception andinterpretation of the robot’s behavior and its intention.
The experiment had a between-participant design, where
each person viewed two videos belonging to the same condition (none, GL, or RH). A Japanese online crowdsourcing
platform1 was employed to recruit participants. We initiallyhired 180 participants, 60 for each condition. In a ques1
Fastask: https://www.fast-ask.com. The website is only available inJapanese.

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tionnaire, we asked three open-ended questions: 1) What
was the robot doing? 2) Did the robot want to communicate
something to the person? If yes, what information did therobot want to communicate? 3) Do you think that the robot
was friendly?

Results(a) corridor_none(b) corridor_GL(c) corridor_RH(d) corner_none(e) corner_GL(f) corner_RH

Figure 3: Screenshots of each
video clip (condition)

After filtering out unreliable data, we had 40 participantsfrom the none condition (11 females, Mage = 39.9, SDage =7.6), 40 participants from the GL condition (14 females,
Mage = 42.3, SDage = 9.3), and 40 participants from the
RH condition (14 females, Mage = 43.6, SDage = 8.6). We
analyzed all their answers and observed interesting findings. We summarize the key findings below.
I In the RH condition, 15 out of 37 participants (41%)
used negative words, such as warning or hostile, todescribe the robot and its behavior. No participants(0 out of 40, 0%) in the none condition and only 1
participant (1 out of 42, 2%) in the GL condition usedsuch descriptions.
II In the RH condition, only 8 out of 37 (22%) participants described the robot as a cleaning robot. However, half of the participants (20 out of 40, 50%) in
the none condition and almost the same number ofparticipants (20 out of 42, 48%) in the GL condition
explicitly described the robot as a cleaning robot.
III In the GL condition, a majority of the participants (32out of 42, 76%) perceived the robot as friendly. In
comparison, half of the participants (19 out of 40,
48%) in the none condition, and, in particular, only 12
participants (12 out of 37, 32%) in the RH condition
perceived the robot in the same way.
IV Participants showed different levels of imagination when explaining what was happening in thevideos. Specifically, participants in the none condition

showed a low level of imagination. They generally described the robot’s behavior in three different ways:
the robot was cleaning, moving (tele-operated), andapproaching the person (intended). However, participants in both the GL and RH conditions showed ahigh level of imagination (described the robot’s behavior in 5 and 6 ways, respectively). People in the
GL condition described the robot as cleaning, moving(tele-operated), approaching the person (intended),
communicating to the person, and playing; people
in the RH condition thought the robot was cleaning,
moving (tele-operated), approaching the person (intended), warning the person, patrolling, and malfunctioning.
V On average, participants used less words to answer
the three open-ended questions in the none condition(83.9 words/person). In comparison, participants in
the GL and RH conditions used 91.7 w/p and 90.9
w/p, respectively.

Discussion and Future Work
Our findings show that RH expressive lights can have a particularly strong negative bias on people’s perception of arobot. People tend to interpret the robot’s behavior in a negative way. In comparison, GL expressive lights can have apositive bias on people’s perception of a robot. Interestingly,
in general, people tended to interpret the robot’s behavior
as goal-directed when it showed either GL or RH. This
indicates that they perceived the robot as having intent (beliefs and desires). As summarized in finding II, far fewer
participants, especially in the RH condition, described theRoomba robot as a cleaning robot, suggesting that theyanthropomorphized the robot in both the GL and RH conditions more than in the none condition. Therefore, we infer

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that social HRI is more likely to be established when a robot
shows expressive lights as a means of communication.
Further, we found that expressive lights can affect how aperson perceives the friendliness of a robot. Summarized infinding III, most participants perceived the Roomba robot as
friendly in the GL condition, and on the contrary, the lowest
number of participants thought so in the RH condition. Thiscan be explained by referring to findings I and IV. Finding IV
particularly lists how participants imagined and interpreted
the scenarios in the videos. Besides the common interpretation that the robot was cleaning, moving (tele-operated),
and approaching the person (intended), participants in the
GL condition additionally imagined that the robot was either
trying to communicate to the person or playing. These interpretations are positive in general, which is probably why
people interpreted the robot as friendly. On the contrary,both findings I and IV clearly suggest that participants in the
RH condition thought that the robot was giving warnings
and was hostile. It is thus not surprising that they treatedthe robot as less friendly.
Claimed by Joseph [10] that a single bit of communication
can leverage an enormous amount of social, cultural, andemotional capital, our study further shows that simple communication based on expressive lights can lead people tocreatively imagine and interpret a robot’s ambiguous behavior. Moreover, such imagination and interpretation is heavily
dependent on the design of expressive lights. In addition,
it can also depend on the contexts and person’s particularknowledge and experience in interacting with robots.
We believe that this work can open up opportunities for further investigation on simple communication methods thatcan be applied to various HRI contexts. Instead of focusing
on specific tasks and goals, this simple method, using expressive lights, relies on people’s imagination and can affecttheir perception and interpretation of a robot at a high level(positive vs. negative). Therefore, future work is needed toinvestigate different HRI contexts and observe how people’s perception and interpretations differ with regard to thecontext. It is also necessary to explore different designsof expressive lights, and their effects on people should be
evaluated. In this pilot study, we did not intend to treat theparameters (color, waveform, and intensity) as independent
factors. However, we suggest future studies should do so to
avoid interaction effects among them.

Acknowledgements
This study was partially supported by JSPS KAKENHI“Cognitive Interaction Design" (No. 26118005).

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