Argumentation-based Inconsistencies Detection for Question ...
Role inconsistencies in elderly care robots -...
Transcript of Role inconsistencies in elderly care robots -...
Role inconsistencies in elderly care robots “You are doing your health exercises well, but I think I will win”
Veron Wormeester - 0758754
Date: 18-03-2014
Supervisors
Raymond Cuijpers
Wijnand IJsselsteijn
Abstract
Future robots will move from doing a single task to doing multiple tasks. This could cause
inconsistencies when the robot adopts a wrong role in a certain task. An experiment has been
performed to see what would happen if a robot would adopt a wrong role in a specific setting. It is
hypothesized that incorrect behavior due to role inconsistency has consequences for the way the
robot is perceived by the participant in terms of trustworthiness and performance. To test this, two
contexts were created: a health exercise context where a fitness exercise guided by the robot had to
be performed, and a game setting. In the game context, a game of Battleships was played against the
robot. The participant would get either the health context or the game context and would do two
trials, one with the role-consistent feedback, and one with the inconsistent role feedback. The results
show that in case of an inconsistency in the roles, the robot is perceived as less safe, less intelligent
and slightly less trustworthy.
Introduction
Having robots in the elderly care is something that seems unavoidable for the future generation of
elderly. The population is aging and the lack of workforce to take care of the people in need of care is
becoming bigger and bigger (WHO, 2007). Elderly care robots could pose a solution for this issue.
Already in 1982, Feigenbaum and McCorduck have suggested to develop a wonderful "geriatric
robot" that could help the elderly as an assistant, coach and companion, all combined into one
"down-home useful" machine, (Feigenbaum & McCorduck, 1982, pp 92-93). Now, more than 30
years later the idea of using robots in elderly care is not science fiction anymore. Well developed and
programmed service robots could aid the care workers with their daily tasks and even replace the
care workers for some of these tasks. Elderly people are capable of staying longer at home with the
aid of a care robot and the amount of robots available for the elderly is virtually unlimited. Living for
a prolonged time at home has benefits for both the elderly care homes as the elderly people
themselves (Tarricone & Tsouros, 2008). Several researchers have been investigating the field of the
care robots and the implications it would have on the way the elderly perceive them, how the robots
should behave and what tasks would be suited for such a robot (Heerink, Kröse, Wielinga & Evers,
2006).
Role adoption under care robots
Assistive care robots can be distinguished in several ways: they could aid the elderly user with their
rehabilitation or they could pose as an assistant or companion. A review of several of these robots
can be found in Broekens, Heerink & Rosendal (2009). According to Broekens et al. (2009), these
identifications are not black and white. Slowly we are progressing to more complex robots capable of
performing multiple tasks. This shift works on multiple levels: a robot nowadays could have the
function of monitoring the person’s health and give health exercises at the same time. But robots in
elderly care should one day adopt several roles to improve the quality of the life of an elderly person
or a care worker. This shift to multiple role adopting robots might lead to a robot being capable of
doing health exercises with the person and serve food later on the day to the elderly person, adapted
to the dietary needs of that person. In this case, the robot acts as a coach and later as a service
robot. It is then also imaginable that a robot could behave as both an assistant and as a companion.
To date, it is still unclear how these roles should be exactly implemented and what is wished for by
the end user (Tapus, Mataric & Scassellati 2006).
There are several frameworks for classifying the role of a robot. Schultz and Goodrich (2007) based
the indication of robot roles on the level of dominance. In this dominance scale, the slave or servant
is positioned on one side and “master” is on the other side. Schultz et al. (2007) mention three
specific types of roles for their explanation: a mentor, peer or slave. Here: the mentor role is the role
a robot could take when it has the function to teach and guide the person, in the case of elderly this
could show in doing fitness exercises, explain about medical subjects or ensure that the person keeps
a healthy lifestyle. A peer role is often found in robots made for comforting the person, companion
robots could be identified as robots behaving in a peer role. Robots in a slave or servant role are
robots that perform tasks for their owner when instructed. It is not unthinkable that someday a
robot built for making and delivering a cup of tea for you will be available on the market (Takahashi,
Nakamura & Hirata, 1998). Other, more elaborate and distinct identifications are identified by
Dautenhahn, Woods, Kaouri, Walters, Koay & Werry (2005) and Scholtz (2002). Dautenhahn et al.
(2005) investigated the roles wished for in an elderly care robot, she used five roles in her research:
assistant, machine, servant, mate and friend. Scholtz (2002) defined role in a broader way by taking
non-autonomous robots into account in his taxonomy as well. Here the following roles were
described: Supervisor, Operator, Mechanic, Peer and Bystander.
Letting the robot adopt several roles in a sequence would greatly benefit the view of people on the
capabilities on robots but could also pose dangers when robots move from a more task oriented
single purpose robot to a context aware multipurpose robot. Because making devices and machines
context aware and the challenges it brings to do so correctly is hard, it is therefore understandable
that making robots this way is a difficult task (Greenberg, 2001; Breazeal, 2003). Robots should not
only be context aware but also make part of that context themselves, this fact will lead to new
challenges that need to be solved before a fully functional robot could replace a human (Fong,
Nourbakhsh & Dautenhahn, 2003). The step from going to robot pets to humanlike servants is very
large, and every detail has to be worked out in order to create a reliable and trustworthy robots
(Graefe & Bischoff, 2003). With that in mind, it is necessary that robots should perform consistent in
regard to the context they are in to ensure convenient interaction (Walters, Syrdal, Dautenhahn,
Boekhorst & Koay, 2008; Riek & Robinson, 2009). This is both of importance in the role the robot
takes in the interaction but also in the way of interacting itself. In the event of an inconsistency the
user might be confused causing a lesser degree of compliance and overall acceptance towards the
robot (Kiesler & Goetz, 2002; Riek & Robinson, 2009). As Riek states:
“The role(s) a robot will adopt during interaction with a user should always be made explicit and
should be immediately apparent. Furthermore, a robot should remain consistent with its advertised
role. A comforting companion robot in a nursing home serving in a peer role should not suddenly
adopt a mentor role and start lecturing residents about their health habits....” (Riek & Robinson,
2009)
This is also shown by Kiesler et al. (2002) who empirically investigated the consistency of robot
appearance and the message it conveys. She found that if the behavior of the robot did not match
the appearance, the acceptability of the robot would decrease. Since the robot is showing behavior
that does not fit into the context of the person, it is likely that the amount of trust in a robot with a
role conflict will decrease as well.
Trust and Performance
Trust is a multidimensional construct that is influenced by a lot of factors and is seen as one of the
most important indicators of successful human-robot communication (Hancock, Billings & Schaefer,
2011). As Hancock and colleagues also mention, the performance of a robot is the primary driver of
trust. For a successful interaction between humans and robots, a certain amount of trust is needed.
Here, the right balance between distrust and too much trust needs to be found. The risk is that if a
person would rely too much on the robot, and would put too much trust in it, it might cause issues
when the robot is malfunctioning. But on the other hand, if the person does not want to comply with
the requests of the robot because of mistrust, then other issues could appear. It is therefore
important that the amount of trust a person has in the robot is well calibrated. Broadbent,
Macdonald, Jago, Juergens & Mazharullah (2007) have shown that people react in different ways to
good or bad performing robots. They found that people reported more negative emotions when the
robot was performing poorly. This shows that inconsistencies in roles could be one factor that
decreases the feeling of trust in a robot. As stated above, the indicators of performance are factors
like behavior, reliability and predictability, and lesser performance could then also cause a lower
amount of trust in a robot.
To further investigate what would happen in case of a role inconsistency and if it indeed hampers
with the perceived performance and the amount of trust in the robot, an empirical study was done
to look into these claims. Expected is that if a robot would show any type of role inconsistency. The
performance and the amount of trust put into the robot is rates lower than if the robot would be
consistent in its role pattern.
Methods
Design
To investigate the claims that the amount of trust and performance in the robot is lower if
inconsistent behavior is shown will be investigated in a 2 (mentor role vs. peer role) x 2 (consistent
behavior vs. inconsistent behavior) mixed design experiment. Here, consistent behavior and
inconsistent role behavior was be tested within participants and the setting of the robot was tested
between the participants. Both these behaviors were tested in an experimental setting with the Nao
robot. The robot either adopted the role of a peer or the role of mentor during a trial. The behavior
change was counterbalanced to limit carryover effects and the effects of fatigue.
The participants were divided into two groups based on random assignment but ensuring a fair
division of age and sex. One group experienced the Nao robot in the mentor role setting; the other
group will experience the Nao robot in a peer setting.
Mentor Role Setting
In the mentor role setting, the Nao robot took the role of a health instructor robot. During this
setting, the robot asked the participant to copy the movements done by the Nao Robot and the
robot gave feedback on the results. The movements to be copied consisted of simple arm bend and
stretch exercises as described in Ebert (2012). Here, the robot first stretches the arms forward and
then bends them downward, repeating this process. In each trial, three different exercises were
performed twice with a small break in between.
Peer Role Setting
In the peer setting, the Nao robot took the role of game companion. During this setting a game of
Battleships was played with the participant. Battleships is a turn based strategy game where two
players have to track and shoot down enemy ships they have positioned on a grid with a predefined
size. This game was chosen because of the equal interaction it promotes; both players are equal in
terms of possibilities in the game. The game is relatively easy compared to other board games played
often by elderly like Scrabble and Rummikub. Besides the equality of interaction and the ease of use
it is also very scalable: the amount of ships and the size of the grid allow for different durations of the
game. The Nao robot used a simple solving algorithm created by the authors for playing Battleships
against the participant and gave suited verbal feedback during the game.
Verbal Feedback
In both settings the verbal feedback of the robot is depending on the condition. In the consistent
behavior condition, the robot used the set of feedback sentences that matched the role the robot. In
the inconsistent behavior condition, the set of feedback sentences from the other setting was used
as the sentences for feedback. Both sets of feedback were made in such a way that the feedback was
meaningful but inappropriate in the wrong setting. All feedback was performed with the robot
looking at the participant while talking. This was done to attract the attention to the robot for the
moment of feedback.
The chosen sentences or the feedback were defined by the authors and were validated through an
online survey. In this online survey, the participants had to rate the sentences on a 7 point Likert
scale about how much they would fit to a health instructor scenario as well to a game buddy
scenario. The sentences were presented in a random order. After the participant had rated the
sentences all sentences had to be labeled to either one scenario in a forced choice questionnaire.
The order of scenarios changed per participant, meaning that one participant would start with rating
the game buddy and the other one with rating the fitness instructor.
After the data gathering, sentences with a less clear preference for a certain scenario were removed
from the list of possible sentences. The mean score of the sentences was used to compare how much
a sentence is valid in one scenario, and unwanted in another. The ten sentences with the biggest
difference in means were chosen as sentences to use. See Table 1.
After the first run of the validation questionnaire spread under friends and acquaintances, 56 people
filled in the questionnaire (31 males, 26 females, average age 25.5 years) a selection of sentences
was found. However, results showed that there was an unequal division in sentences that would fit
to one of the roles, and more suitable sentences needed to be found. A second questionnaire with
more sentences was created and spread out on Facebook groups for a bigger sample size. 195 people
filled in the second questionnaire (40 males, 155 females, average age 39.1 years). There were no
differences found between the male group and the female group, so we averaged across gender. The
results are shown in Table 1. Due to the fact that the sentence “Heel goed, ga zo door” was not
explicitly tested, but is derived from the very similar sentence “Heel goed, probeer zo door te gaan”,
its mean score at the fitness instructor scenario was 6.69 and 95% of the people indicated it would
belong to the fitness instructor. The sentence is included to make the sentence more meaningful in
the inconsistent health exercise context.
Table 1: Chosen sentences and the mean difference between the subjective rating of appropriateness
between the settings
Used Sentence Translated Sentence Mean Difference
Mentor Role (Fitness setting)
Je wordt er gezond en fit van You are getting healthy and fit from it 2,797
Probeer je beste te doen Try to do your best 1,847
Je mag nu even op adem komen You can now catch a breath for a moment
2,900
Heel goed, probeer zo door te gaan Very good, try to continue like this 1,984
Heel goed, ga zo door! Very good, continue like this! *
Dit was alles wat ik met je wilde doen vandaag
This is all that I wanted to do with you today
2,067
Peer Role (Game setting)
Ik vraag me af of ik het goed doe I am wondering if I am doing well 1,812
Ik snap het niet I don’t get it 2,312
Ik vraag me af hoe ik het doe I am wondering how I am doing 1,262
Ik weet niet wat ik nu het beste kan doen I don’t know what I could do best now
3,000
Ik vind het moeilijk I find it hard 1,926
Ik vind het leuk om dit met je te doen I like to do this with you
1,221
Ik denk dat ik dit ga winnen I think I will win this 2,332
Dependent Variables
As the dependent variables for the experiment, the amount of trust were measured along the five
scales of the Godspeed Questionnaire (Bartneck, Croft, Kulic, & Zoghbi, 2009): anthropomorphism,
animacy, likeability, perceived intelligence and safety. The Godspeed questionnaire was used to
investigate the effects caused by the role inconsistencies on attitude towards robots. The amount of
trust was measured using the trust in automated systems questionnaire designed by Jian (2000).
Participants and Task
As the primary goal of the service robot is addressed to the elderly, the experiment was conducted
with elderly people as well. 40 participants in the age group of 50+ were recruited for this
experiment through local Facebook groups, handing out flyers on the market and the participant
database of the University of Eindhoven. Over half of the participants had no previous contact with
the robot before and previous knowledge was taken into account during the research. The
participants were rewarded with a monetary incentive as a thank you for their time.
Depending on the condition, the task the participants had to do was either to follow fitness
instructions by the robot or play battleship with the robot. In the fitness exercise the participant was
asked to copy the robots movements while the robot gave verbal feedback on their performance. In
the game exercise, the robot asked the participant to play a game with him/her. Here, a game of
Battleships was played with the participant. Both tasks ended with a questionnaire the participants
had to fill out. These tasks were done twice in an experiment.
40 participants aged between 49 and 76 (µ: 62.95, sd: 7.79) participated in the experiment of which
20 males and 20 females. 26 indicated they had participated in previous experiments with the robot.
Both contexts were performed by 20 persons of which 10 males and 10 females.
Apparatus and Materials
The Nao Robot by Aldebaran Robotics was used for this experiment. This is a 57cm tall humanoid
robot capable of walking, talking, gesturing and looking around, and has in total 25 degrees of
freedom. The Nao robot can use speech synthesis to talk but is also able to play prerecorded speech.
For this experiment, prerecorded speech was used as speech synthesis in Dutch was not available for
the Nao robot at the time of the experiment. The speech used was created by text to speech
software and was preloaded on the robot before the experiment. For the health exercise mentor
role, a simplified adaptation of Ebert’s implementation for the Nao was be used with two different
health exercises (Ebert, 2012). For the game setting, where the robot adopted a peer role, a simple
BattleShips script created by the author himself was implemented. A Wizard of Oz technique was
used to handle the speech recognition part and to help the participant whenever he or she got stuck.
The experimenter remotely controlled the robot through its inbuilt Wi-Fi connection.
The experiment was conducted in the UseLab of the University of Eindhoven, this lab resembles a
living room and contains a sofa, television, cabinets with books and indirect lighting. The Nao Robot
was placed on the ground on an open space in the health exercise setting and on the Table in the
game condition. For the health exercise the participant was standing on the opposite side of the
open space facing the robot. For the game setting the participant was seated on the couch with the
robot and the game in front of him/her.
Procedure
During the experiment, the experimenter welcomed the participant inside of the UseLab and gave
the participant the informed consent form to fill in. After this form had been filled in, a short
introduction to the robot was given. The participant was then either seated on the Table for the
game settings or guided to a chair in the room for the health exercise setting. Then he or she
interacted with the robot. For the health exercise setting this was a fixed time of 12 minutes. For the
game setting the time of the experiment lasted between 12 and 20 minutes, depending on the
progress of the game. During the interaction with the robot, the robot gave at least ten times verbal
feedback on the progress. After the session, the participant was guided back to the computer to fill in
the questionnaires. When finished with the questionnaire the second and last robot session started.
This session was identical with the first one with the exception that the other set of feedback
sentences was used. Afterwards, after the participant filled out the questionnaire again, the
participant was thanked for their time and received €7.50.
Results
Consistency check
The scales used were checked for internal consistency, the Trust in Automated Systems
questionnaire consisted of 14 items (α = .87). The Godspeed questionnaire consists of 5 scales: the
anthropomorphism consists of 5 items (α = .90), animacy consists of 5 items (α = .76), likeability
consists of 5 items (α = .87), perceived intelligence consists of 5 items (α = .86), safety consists of
three items (α = .539). One item (Calm) was therefore removed from the safety scale as it negatively
influenced the Cronbachs alpha, without it the new α = 0.73.
Trust
The trust questionnaire was filled in
completely by 36 participants. The trust
scores were calculated and after further
inspection on the scores, one outlier was
removed (Z-Score on the difference in trust
between the contexts: -4.06) leaving the
results of 35 participants. The results can be
found in Figure 1 and Table 2. Repeated
Measures ANOVA was performed to see if
the amount of trust between both samples
would change. A trend can be spotted
between the consistent feedback and the inconsistent feedback (F(1,33) = 3,066, p = .089). No
interaction effects were found. A check was made to see if order effects occurred, this turned out to
be not the case (p = .68).
Table 2: Difference in trust between contexts
n µ
Consistent SD
Consistent µ
Inconsistent SD
Inconsistent µ Diff.
SD Diff.
Sig.
Health Exercise 18 5,22 1,09 4,98 1,13 -0,24 0,77 0,238
Game 17 5,43 0,94 5,21 0,78 -0,22 0,77 0,228
Combined 35 5,33 1,01 5,10 0,97 -0,23 0,76 0,089
Figure 1: In both settings, the amount of trust decreases slightly in the Inconsistent condition. Error bars +/- 1 SE
Godspeed
The Godspeed questionnaire was filled in completely by 36 participants, some participants did not fill
in the complete questionnaire but their data was usable for some scales that fit within the Godspeed
questionnaire. No extra outliers were detected during the analysis.
A repeated measures ANOVA was performed on the data with both contexts and consistencies, no
significant results were found for Anthropomorphism, Animacy and Likeability. A main effect on
Perceived Intelligence ( F(1,35) = 8.448, p = .006 ) and Perceived Safety ( F(1,35) = 5.441, p = .025 )
was found, both perceived intelligence as perceived safety are significantly lower when the robot
used inconsistent feedback. The interaction effects for perceived intelligence and perceived safety
show that there is no significant difference between the different settings (intelligence: F(1,35) =
2.167, p = .15, safety F(1,37) = 0.33, p = .57). The difference between the other scales turned out to
be not significant and was not further investigated. Because correlations were found between
Godspeed results and perceived trust, a regression analysis was performed to see if the Godspeed
variables could predict trust. With the consistent feedback, it was found that two predictors
explained 52% of the variance (r² = .55, adjusted r² = .52, f(5,34) = 19.668 p < .01). It was found that
likeability (β = .83, p < .05) and perceived intelligence (β =.47, p <.06) significantly predicted
perceived trust. The other variables were found to be not significant. With the inconsistent
feedback, it was found that only likeability could significantly explain 34% of the variance (r²=.43,
adjusted r²=.34, f(5,35) = 4,589, p < .01, β = .97, p < .01).
Table 3: The results of the Godspeed Scales in the Health and Game contexts
n µ
Consistent SD
Consistent µ
Inconsistent SD
Inconsistent µ
Diff. SD
Diff. Sig.
Anthropomorphism 38 2,37 1,05 2,36 0,93 -0,01 0,50 0,90
Animacy 39 2,53 ,818 2,59 0,91 0,06 0,44 0,40
Likeability 38 3,70 ,733 3,68 0,77 -0,16 0,40 0,81
Intelligence 37 3,35 ,76 3,14 0,77 -0,21 0,43 0,006
Safety 39 3,87 ,77 3,64 1,01 -0,23 0,62 0,025
Figure 2: Results from the Godspeed scales per context, the perceived intelligence drops in the health context but stays equal
in the game context. Error bars = +/- 1 SE
Comparing the tasks, no significant differences were found between them. The order of presenting
however, did matter for the amount of anthropomorphism (µ difference: -0.34, p = .04), showing
that a small order of presenting effect has happened and that the amount of anthropomorphism
decreased in the second meeting with the robot. See also Table 3 and Figure 2.
From the extra questions added to the questionnaires, an increase in perceived bossiness could be
found in the game setting when the inconsistent feedback was given. (µ difference: 1.10, p = .053).
No changes were found in the health exercise context. See Table 4 and Figure 3.
Also, participants felt in general more at ease with the mentor feedback set compared to the game
feedback set, which follows from the fact that the feeling of being at ease was lower in both
contexts. The feeling of being at ease dropped significantly in the health context (t(18) = 2.249, p =
.037), the rise of the feeling of being at ease in the game setting was not significant (t(19) = -1.629, p
= 0.12).
Table 4: Perceived amount of bossiness per context
µ
Consistent SD
Consistent µ
Inconsistent SD
Inconsistent µ
Diff. SD Diff. Sig.
Health 2,53 1,611 2,30 1,342 -,211 1,813 0,485
Game 2,20 1,240 3,30 1,780 1,100 1,586 0,053
Figure 3: The perceived bossiness increases in the game context and decreases in the health context, showing that the
feedback in the health condition is being perceived as more bossy
Discussion
After testing human-robot interaction in two contexts it is clear that the right context does matter in
how a robot is perceived. The results show that putting a robot into a role that is not suited to the
context has influence on the perceived intelligence of the robot, the perceived safety and the
amount of trust in the robot. We found that, in line with our hypothesis, the users rated the robot as
less safe, less intelligent and slightly less trustworthy. For perceived intelligence, the inconsistent
behavior that the robot shows that people in general notice that something is odd and out of
context, and as they cannot put the feedback into context, they assume that it must be the robot
itself that is talking nonsense. This also explains why the perceived safety decreases with inconsistent
behavior. Although less direct, perceived safety is dependent on the predictability of a piece of
technology. Kulic & Croft (2005) already showed this in their research about the design of robots.
They measured through physiological measurements the amount of surprise a person exerted and
the amount of anxiety a person has. It appeared that the amount of surprise was a good predictor for
the amount of anxiety. While the robot used feedback inconsistent with its behavior, the message
the robot told changed both in the game setting as in the health exercise setting from something that
would fit in the context to an out of context sentence. These sentences were often perceived as odd
and either caused people to laugh about the robot or caused people to have some additional
questions about it. As with perceived safety and perceived intelligence, it is therefore
understandable that the perceived amount of trust exerted from the robot is decreasing as well.
Although not strong, the effect found is consistent over both contexts, meaning that further
investigation in the field of trust and role inconsistencies is needed to make this claim valid. The
reason why this effect is less strong compared to the perceived intelligence and perceived safety
could be attributed to the fact that trust is a multidimensional construct, and is influenced by other
factors like intelligence and likeability. As found, the perceived intelligence decreases with
inconsistent feedback but the likeability does not change. Since likeability is such a strong predictor
for trust, it is therefore understandable that the perceived trust did not decrease with the same
levels as perceived intelligence.
The clear change in perceived bossiness shows that the manipulation worked. The increase in
amount of bossiness in the game session shows this. This is in line with expectations, and can be
explained by the fact that coaching during a competitive game is less accepted behavior. People felt
less at ease with inconsistent feedback in the health exercise condition, this is probably due to the
way feedback was given. As the health exercise consisted mostly of one way interaction, statements
like “I don’t know what do to do next” caused some awkward silences under participants. Having two
way interaction in the health exercise would probably have caused this effect to disappear.
This research implicates that robots that are able to adopt more than one role have to be built in
such way that contextual discrepancies are being held as small as possible. Even though context
awareness is a hard subject to tackle it is an important one. This is definitely the case if a robot is
being used in the elderly care and gets responsibilities like reminding elderly to take their medicine.
The findings in this study have some limitations. Firstly, every subject only received one context and
were presented with feedback that they could not place in the situation as they did not know where
the feedback actually belonged. Further investigation is needed to see if the effect would hold in a
broader context. Also, the participants spent only a small time with the robot. It could be expected
that a longer time has influence in the way the communication goes between robot and human.
Here, a small nuisance that is happening more often could grow into a point of irritation, which
would enhance the feeling of distrust. This could only be researched when the robot used is capable
enough.
Creating multipurpose robots that will adopt multiple roles is a challenge that will take a long time
before it will be a “down-home useful machine” and would meet its expectations. Before we reach
this stage, robots will have its imperfections and its flaws, which will have consequences in how we
see and perceive them, but that might just make them a bit more humanlike.
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