This is a preprint of an article published in International Journal of Social Robotics.
The final authenticated version is available online at: https://doi.org/10.1007/s12369-021-00792-8

Help-Estimator: Robot Requests for Help from Humans by
Estimating a Person’s Subjective Time
Mitsuhiko Kimoto · Yuuki Yasumatsu · Michita Imai

Received: date / Accepted: date

Abstract In human-robot cooperation, it is effective
for a robot to ask a human partner for help to reduce
the time required for a task. However, the person might
not be able to help the robot if she or he is engaged
in something else. In this study, we focused on the
person’s subjective estimation of the time for a task
and investigated its effect on the likelihood of agreeing
to help the robot. For this purpose, we developed a
Help-Estimator system that decides whether the robot
should request help by considering the required time
for both the person and the robot to finish their tasks.
We conducted an experiment to evaluate such help
requests that incorporate the human’s subjective time
estimation for a task. We found that appropriate timing
for requesting help, as estimated from the person’s
viewpoint, increases the likelihood of the person helping
the robot and improves the person’s impression of therobot as a partner.
Keywords Human-robot interaction · Human-robot
cooperation · Help request

1 Introduction
Social robots provide services in real environments
such as shopping malls [1], museums [2, 3], andairports [4]. We expect such robots to be sociable andcooperative, and not merely communicative. However,further improvements will be needed to achieve actual
human-robot cooperation. For example, a person and arobot may clean a room together. One of the benefits
M. Kimoto, Y. Yasumatsu and M. Imai
Keio University, 3-14-4 Hiyoshi, Kohoku-ku, Yokohama,
Kanagawa 223-8522, Japan
E-mail: {kimoto, yasumatsu, michita}@ailab.ics.keio.ac.jp

of using a cooperative robot is the time saved. [5]suggested that cooperative work between a humanand a robot can be evaluating by measuring the idle
time while they perform a task. There are various
research works on improving such cooperative work by
reducing the task execution time. [6, 7] proposed aninteraction design that enables the robot to help the
person proactively to reduce the task time. [8] proposeda system that schedules the robot’s behavior to help
the human partner smoothly without the robot being
idle. Although these studies were intended to make therobot help the human, there is another way to reducethe cooperative task time—i.e., the person can help therobot. Hence, this paper deals with a situation in whicha robot requests help from a human partner to reduce
the required time in a human-robot cooperative task.
Indeed, some previous works studied the behavioral
design for a robot to be helped by a person [9, 10]. For
example, it is important to design what the robot says
[9] and how it initiates the interaction with a person[10]. In these studies [9, 10], the timing for the robot
to ask for help was based on a situation in which therobot could not perform its own task by itself. However,
the required task execution time can also be reducedby asking the person for help even if when the robot
does not have to deal with a setback in its own task.
This leads to a question of whether people would help
a robot even when it seems able to perform its task on
its own. For such situations, we assume that people’s
likelihood of helping a robot depends on their state andsituation: the ability to perform the requested task, thetime required for finishing the task, and their nature(kindness), because there is no requirement for a person
to help a robot in a situation in which the robot canperform on its own. Moreover, as shown in Figure 1,
the person may have reasons not to help the robot even2

Mitsuhiko Kimoto et al.

task, and the time that humans spend doing their own
task. Section 5 explains our experiment and hypotheses.
Section 6 reports the experimental results and evaluatesthem in terms of the person’s likelihood of helping therobot and the robot’s impression on the person. Section
7 discusses the results and describes the limitations of
this study. Finally, Section 8 concludes the paper andmentions future work.
Fig. 1 Robot requesting help in human-robot cooperation: therobot asks the human partner for help to reduce the timerequired for the task (bottom right: required time estimatedby the robot); the person, however, might not help the robot.

2 Related Work
2.1 Human-Robot Cooperation

if the robot decides that requesting help will make thecooperative work more eﬀicient. It is thus a key issue
for the robot to estimate the nature and circumstancesof people who are asked for help. In addition, if therobot requests help repeatedly, it might lose credibility
as a partner. For the robot to become a good partner, it
should stop requesting help from a person who does notseem receptive to it, even if the robot sincerely needs
help.
In this paper, we investigate whether the likelihoodof a person accepting a robot’s request for help
can be increased by having the robot consider the
person’s subjective estimation of the time required
for a task. To investigate the effects of consideringa person’s subjective time estimation, we developed
an experimental system called Help-Estimator, whichfinds a subtask for which a robot should request
help from a human. Help-Estimator determines thesubtask according to two processes: (1) it finds a
useful subtask to reduce the time needed to complete
a human-robot cooperative task, and (2) it deduces
the human’s likelihood of agreeing to help. A suitablesubtask is found by calculating the amount of work
to be completed in the overall task. The likelihood ofagreeing to help is found by estimating how much busier
the person feels in comparison to the robot during thecooperative task.
The paper is structured as follows. Section 2
describes related work in the context of human-robot
cooperation and help requests from robots. Section 3
describes a preliminary case study that we conducted toinvestigate how people decide whether to help a robot,
and it explains the problem that we address in thisstudy. Section 4 describes the Help-Estimator systemused in the experiment. In particular, it explains how
Help-Estimator finds a suitable subtask and estimates
the likelihood of the person agreeing to the robot’s help
request. Section 4 also explains how the robot assesses
two time periods: the time that it spends doing its own

Increasingtheeﬀiciencyofhuman-robot
cooperation is critical in the human-robot interaction
field, and there have been various research works on
this topic. For instance, Hoffman proposed metrics toevaluate the fluency of collaboration [5]. Other works
apply rational actions to achieve smooth collaboration.
For example, collaboration can be facilitated by having
robots proactively assist people [6] by speculatingon their intentions [7][11] and taking understandable
actions [12]. A robot’s actions can be scheduled toadapt to the human partner and thus reduce thehuman’s idle time [8]. Collaboration can be improved
through task planning by using a daisy graph [13] andlearning of human user models by using MOMDP [14].
Additionally, team performance can be improved byhaving a robot explain its own behavior [15].
Despite many such approaches to facilitate humanrobot cooperation, there has been no research on
reducing the duration of a cooperative task by having
people help robots with their tasks instead.

2.2 Robot Asking for Help
In human-robot cooperation, robots typicallysupport people; however, there are situations in which a
robot cannot perform its given task. In such a situation,
the robot must ask people for help to accomplish its
task. For example, the robot might not be able toachieve the task [16] or reach its destination [17] on
its own.
Many research works have studied the behavioral
design of a robot asking people for help. For
example, researchers have studied speech [9] andnonverbal behavior [18] as methods of requesting help.
Additionally, there are studies on initiating interactions[9], promoting the attitude in interactions [19], andadjusting the interactive timing [20] when robots
request help. To investigate whether people will help
robots, researchers have observed human reactions toHelp-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time

3

3.1 Setting

Fig. 2 Illustration of the preliminary case study

robot malfunctions [21] and failures [22]. In addition,
empathy toward a robot encourages people to help it[23]. From the viewpoint of social psychology, Cameron
explored a model of cooperation in which humans help
robots [24]. Moreover, a robot that made errors was
revealed to be more likable than a robot that performed
perfectly [25]. This result suggests that a robot’s help
request probably does not make a bad impression onhumans even when it cannot perform its task.
Other related works have focused on situations inwhich a robot asks for help from a nearby person who
is not currently doing her or his own task or in which a
robot’s task is part of a cooperative task with a person.
In such situations, there is no need for the person tointerrupt her or his own work to help the robot. In
contrast, in this research we focus on a situation inwhich a robot and a person work on a cooperative
task but separately handle their individual tasks, withno requirement for the person to help the robot. For
example, suppose that a person and a robot are cleaning
a room together. The person might consider the robot’s
request for help to have merit, because if it gets help
with a task that is time-consuming on its own, thecooperative task (e.g., cleaning) can be finished sooner.
For such situations, to the best of our knowledge,
no studies have investigated the robot’s timing inasking for help by considering the person’s subjective
estimation of the time required for the task.

3 Case Study
To investigate how people decide to help robots,
we investigated a preliminary case study with four
participants.

Figure 2 shows the environment of the case study.
The robot’s task was to put four items of differently
colored trash in a trash box, while the humanparticipant’s task was to put tiny beans in a bowl. The
number of beans was adjusted so that the participant
could finish her or his task before the robot could finish
its task. The goal was to finish the cooperative task
early, which required the robot to ask the participant
for help to reduce its task time. When the participant
had put all the beans in the bowl and all the trash wasin the trash box, the trial was finished. To avoid errors
by the robot, we used a Wizard-of-Oz-type scenario
in which an operator manipulated the robot remotelyfrom behind a partition so as not to be seen by theparticipant.
Before starting the case study, we gave the following
instructions and information to the participants.
– We described the case study and the task’s
termination condition.
– We explained the cooperative task’s goal to haveboth the participant and the robot finish their tasks
early.
– During the task, the robot asked the participant
to help by putting trash in the trash box. The
participant could decide whether to help the robot.
If the participant decided to help the robot, thenshe or he immediately stopped the individual task
and helped the robot. If not, the participant ignored
the robot and continued her or his own task.
– Neither the participant nor the robot intervenedin the other’s task unless the robot asked forhelp. That is, even if the participant finished theindividual task earlier, she or he was not to help
the robot unless it asked for help. We also informed
the participants that they should decide whether tohelp the robot according to the goal of having themboth finish their tasks early.
After receiving the instructions, the participants
started the task. The timing of when the robot pickedup each trash item and asked for help was decided inadvance, as follows: when the robot put the red trashitem in the trash box, it asked the participant to put the
purple item in the box; then, when it approached theyellow item, it asked the participant to put the green
item in the box. If the participant did not agree to pickup a trash item, the robot did not ask her or him topick up that trash again. Thus, we observed whether theparticipants picked up the two trash items for which therobot asked for help. After each trial, we interviewed
the participant and asked why she or he did or did not
help the robot.

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Mitsuhiko Kimoto et al.

3.2 Results
All four participants responded to the first request
for help and picked up the purple trash item. However,none of them responded to the second request to pickup the green item. All four participants’ reasons forthese decisions were related to their subjective time
estimations. The reason for helping the robot was that“the purple trash item was far from the robot” or“the robot seemed to be taking more time than me.”Similarly, the reason for not helping the robot was that
“I thought I was taking more time than the robot” or“I hoped the robot would work harder.” These reasonsindicate that each participant decided whether to help
the robot according to the estimated time to finish heror his own task. The key point is that the time here
was not the actual time but the participant’s estimatedtime according to a subjective perspective.

Fig. 3 Example of a cooperative task

3.4 Example Environment for Cooperative Task
As a first step toward such estimation of task times,
this paper focuses on the cooperative task used in thecase study, as shown in Figure 3. We chose this task
because it is easy to estimate the time required for eachindividual task by measuring the mass of beans in thebowl and the distances between the robot and the trashitems.

3.3 Problem to Solve
4 Help-Estimator
The case study results indicated that people did
not always help the robot even when it asked for help.
Therefore, it might be a waste of time for the robot torequest help when the person seems unwilling to help.
On the other hand, if the robot only requests help when
the person seems likely to help, it can avoid wastingtime on fruitless requests.
As described above, we assume that people decide
whether to help a robot according to their subjective
estimation of the time needed to finish a task. Thus, in acooperative task, if the robot can estimate the person’s
subjective estimation of the time to finish her or histask, it can avoid asking for help when she or he isunlikely to help.
Various works in psychology have examined theperception of time flow (reviewed in [26]). In thehuman-robot interaction field, [27] focused on theeffects of people’s emotional states on time perception
and proposed a robot that predicts how each person
experiences the flow of time and accordingly prioritizes
tasks requested by humans. However, there is no workon robots deciding whether to ask for help according toa human’s subjective time estimation for a task.
To investigate the effects of a robot incorporatinga person’s subjective view of the time to finish a task
in deciding whether to request help, we first need amethod for a robot to estimate how long a person thinks
it will take to finish a task and to decide an appropriate
time to request help.

In this section, we describe a proposed system called“Help-Estimator,” which we developed to investigate
the effects of incorporating a person’s subjective time
estimation for a task into the model of a robot thatrequests help. Help-Estimator enables the robot to ask
a person for help by estimating the subjective timethat the person estimates it will take to complete heror his own task. Help-Estimator has two functions: itchooses robot subtasks with a required time that can be
reduced by getting help from the person (i.e., finding asuitable subtask), and it estimates the acceptability ofa robot’s request for help (i.e., estimating help request
acceptability). The robot generates a help request
depending on the estimated acceptability, which ideallymeans that the person will have time to help.

4.1 System Architecture
Figure 4 shows a schematic of Help-Estimator,
which consists of two time estimation (TE) modules
and one help module. The time estimation module
based on the robot’s viewpoint (robot TE module)
estimates the time required for both the person andthe robot to finish their own tasks, with the estimation
based on their physical activities. The time estimation
module based on the person’s viewpoint (person TE
module) estimates how long the person thinks it willtake for she or he and the robot to finish their tasks,

Help-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time5

errors in the experiment reported here, the robot was
operated by a human in a Wizard-of-Oz scenario.
4.2 Help Module
4.2.1 Finding Suitable Subtask

Fig. 4 Schematic of Help-Estimatorwith the estimation based on her or his subjective
viewpoint. Finally, the help module selects a subtask
for which the robot should ask for help from theperson, according to the time estimation by the two
TE modules.
The robot TE module estimates the time from therobot’s viewpoint by measuring the remaining physicalcharacteristics of the individual tasks. First, trh denotes
the time estimated by the robot for the person to finish
her or his task. In the cooperative task used here, it iscalculated from the remaining mass of beans and theaverage speed at which the person has picked them up.
Second, trr denotes the time estimated by the robot
for it to finish its own task. Here, it is determinedby measuring the distances between the robot andthe remaining trash items and the angles between the
direction it is facing and the bearings to the items.
The person TE estimates the time from the person’s
viewpoint by using two estimation criteria for the
progress of her or his own task and for the progress of
the robot’s task. The first criterion is used to estimate
thh , which denotes how long the person thinks it willtake to finish her or his own task. The second criterion
is used to estimate thr , which denotes how long she or
he thinks it will takes the robot to finish its task. We′ and t ′ to denote the results of estimating t
use thhhhhr
and thr , respectively.
Both TE modules need information on the progressof the person’s and robot’s tasks. We measure therobot’s progress by using a fixed-point camera to obtain
the position coordinates of the robot, the trash items,
and the trash box. We measure the person’s progressby using an electric scale to obtain the mass of beansin the bowl.
Figure 4 also shows a route planning module, which
creates the route for the robot to perform its task andsends commands to the robot to guide it along thecreated route. Note again that to avoid robot behavioral

First, the help module chooses the robot’s subtask
for which it should request help. The candidate
subtasks are selected according to the robot’s
viewpoint, which means that receiving help from theperson should reduce the time for the robot to finish
these subtasks. For example, in Figure 3, the subtasks
correspond to the trash items. The robot’s task time
will be significantly reduced if the person throws away
the item that is farthest from the robot.
Because the person and the robot perform theirown tasks simultaneously, the time T required for thecooperative task is derived from trh and trr :T = max(trh ,trr ).

(1)

The help module also calculates the expected total
time To if the robot requests help from the person for
subtask o and she or he performs it instead of the robot:
To = max(trh + ho ,trr − to + ho ).

(2)Here, ho denotes the total time for the robot to generatea help request for subtask o and the person to performthat subtask, and to denotes the required time for the
robot to perform subtask o by itself.
Next, Ω denotes the subtask that minimizes thetime required for the task when the person helps with
it. The help module selects Ω according to the following
equation:Ω = arg min To .

(3)o∈taskWhen TΩ > T , the robot does not request help, because
obtaining help with Ω would not reduce the overall task
time.
4.2.2 Estimating Help Request Acceptability
After selecting a suitable subtask Ω , the help
module decides whether the robot should request helpfor Ω . The results of the preliminary case study
described in Section 3 indicated that the participants
agreed to help the robot when they thought they
could finish their task earlier than the robot could
finish its task. Hence, we use this empirical result asa criterion for estimating the acceptability of a robot’s
help request, as follows:
′
′
thr
− thh
− hΩ > 0,(4)

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Mitsuhiko Kimoto et al.

Algorithm 1 Creating the robot’s routeFig. 5 Examples of the robot’s routes

where hΩ denotes the time it will take the person to help
the robot with Ω . Thus, the robot does not request help
when condition (4) is not met.
4.3 Robot TE Module
As described above, the TE module based on the
robot’s viewpoint estimates trh and trr by measuring theremaining physical characteristics of the person’s task
and the robot’s task, respectively.
4.3.1 Estimating trh
The robot TE module estimates trh by estimating
the remaining number of beans and using the average
speed c for the person to pick up a bean. As the person
picks up beans, c is updated according to the following
equation:
n
c= ,
t

N −n
.
c

Algorithm 2 Defining the route from start to goal
function route(start, goal)
l: line segment from start to goal
if l crosses trashBox thenset path to detour to trashBox
else
path = {goal − start}
end ifreturn pathend function(5)

where n is the number of beans picked up, and t is thetime from the start of the cooperative task. To obtainn, we place an electric scale under the bowl to measure
the mass of beans picked up. Then, we estimate n fromthe mass by using the average mass of 100 beans.
Next, trh is calculated by the following equation,
where N is the total number of beans to be picked up:
trh =function robotRouteζ = {}
for each trash item o do
if o is in trashBox thencontinueelse if o is target thenif robot possesses o then
ζ + = routeToBox(o)elseζ + = route(robot, o) + routeToBox(o)end ifelse
ζ + = route(trashBox, o) + routeToBox(o)end ifend forreturn ζ
end function(6)

4.3.2 Estimating trr
Because the robot uses an algorithm todeterministically create a route to the trash, trr isdetermined according to the route distance D and thetotal turning angle Φ , which determine the total travel
time due to the robot’s walking and turning actions.

Algorithm 3 Defining the route from start to the trashbox
function routeToBox(start)
goalLine: open side of trashBox
if start.x < goalLine thenpath = {goalLine − start}else
set path to detour to trashBox
end iffor p in path doset p to space in direction of p → next
if route(p, p → next) passes o then
add new point p∗ between p
and p → next to detour to oend ifend forreturn pathend function

4.3.3 Creating Routerobot to move the trash easily. Then, it guides the robot
to the trash box.

The route planning module uses Algorithm 1 tocreate a route ζ for the robot to put the trash itemsin the trash box. The route is defined as a set of points
that are subgoals for the robot to reach. It guides therobot to a position behind each trash item to allow the

Algorithm 1 generates ζ by combining a path fromthe robot to a trash item, generated by Algorithm
2, and a path from the trash item to the trash box,
generated by Algorithm 3. Figure 5 shows examples ofroutes generated by these algorithms.

Help-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time

7

4.3.4 Calculating Required TimeThe required time trr for the robot’s task is
calculated from the generated route. Given ζ ={p0 , p1 , ..., pm }, where each point pi is a two-dimensional
vector, the total distance D and total angle Φ arecalculated as
m−1

D = ∑ dist(pi , pi+1 ),

(7)

i=0

m−2

Φ = ∑ | arctan(pi+2 − pi+1 ) − arctan(pi+1 − pi )|,

(8)

i=0

where dist(pi , pi+1 ) is the distance from pi to pi+1 .
Finally, given the robot’s velocity v and angularvelocity ω , trr is calculated as follows:
trr =

D Φ
+ .
v ω

(9)

4.4 Person TE Module
The TE module based on the person’s viewpoint
estimates thh and thr by deducing how much progress the
person thinks that she or he and the robot have made on
their tasks. Thus, this section investigates how people
subjectively estimate the time required for each task,
and it develops models to more accurately estimate theexpected time. We also verify that the models properly
estimate a person’s subjective sense of time for the tasks
used here.
4.4.1 How People Estimate Time for Own Task
We conducted an experiment to investigate how
people estimate the remaining time for their own task.
We asked eight participants to each pick up 200 beans,
and we measured the time for each to perform the task.
Before starting the task, we asked the participants how
long they thought it would take them to finish the task.
Then, every 10 beans, we asked them to estimate theremaining time needed, and this was repeated until thetask was finished. After the experiment, we asked theparticipants how they estimated the time required forthe task.
The results indicated that the participants
estimated the remaining time from the remaining
number of beans. However, this estimation is different
from the robot’s estimation of trh , which is completely
based on physical characteristics, even though theparticipants referred to the remaining number ofbeans. At the beginning of the task, they constantly
decreased the estimated required time by a certain′ and tFig. 6 Comparison between thh
hh for one participant

amount, regardless of their initial expectations. Then,they corrected the expected time several times when
only two-thirds to one-half of the beans were left,
because they thought the task was taking more orless time than they had expected. After correction,
they decreased the time by a certain amount again.
Finally, they correctly determined the required time
when about 30 beans were left.
4.4.2 Estimating thh
From the above findings, we defined a function
pred_thh (n) that estimates thh in terms of n, theremaining number of beans, as follows.


n>τ

tH − vh (N − n) tH −vh (N−τ )−trh + tn=τrh2
pred_thh (n) = pred_thh (τ ) − vh (τ − n) 30 < n < τn · pred_t (30)/30
n < 30hh(10)Here, N is the total number of beans, tH is theinitial time that the person expects before starting thetask, and vh is the time taken to pick up one bean.
Finally, τ is the timing to adjust the expected time,as the participants did in the experiment described insubsection 4.4.2; we set τ = 2N/3.
′
4.4.3 Accuracy of Expected thh′ to tFigure 6 shows a graph that compares thhhh
for the data obtained in the experiment described insubsection 4.4.2. The graph indicates how well Equation(10) estimates a person’s subjective perception of thetime required for the task of picking up beans. The
average error was ±25.57 s, and the correlation was
0.947. Thus, because of its accuracy, we use thisequation as the person’s criterion for estimating the
progress of her or his own task.

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4.4.4 How People Estimate Time for Robot’s Task
We also investigate how people estimate theremaining time thr for the robot to finish its task,
and we develop a model to estimate thr and verify its
accuracy. We again conducted an experiment, in which
five participants observed the robot performing its task
and estimated how long it would take to finish. As in
our preliminary case study, the robot was operated
by a human in a Wizard-of-Oz manner. The robot
walked and stopped once every four steps that it took
(we refer to this as a step set). In each step, therobot could take one of the following actions: walk
forward, walk backward, turn left, or turn right. To
avoid giving the participants an obstructive effect due
to fluctuations in the robot’s action timing, it accepted
a command from the human operator every 4 seconds.
By having the robot’s actions based on a constantinterval, the participants could more easily estimate thetime required for its task.
Before starting the task, we asked the participants
how long they though it would take the robot to finish
the task. Then, every time the robot performed one step
set, we asked the participants to estimate the remaining
time for the robot’s task, and this was repeated until
the robot’s task was finished. After the experiment,
we asked the participants how they estimated the timerequired for the robot’s task.
The results indicated that the participants
estimated the remaining time according to how far
the robot moved in one walking step and how manydegrees it turned left or right in one turning step.
They decreased the expected time when there wasno disruption in the robot’s movement. On the other
hand, they increased the expected time when there wasa disruption, such as the robot performing differently
from their expectation or trash kicked by the robot
rolling away from its course.
The way the participants expected the robot’s task
time to be based on its movement is similar to therobot’s estimation for its own task, as described insubsection 4.3.4. The primary difference is that theparticipants did not know the robot’s exact route andthus used the speed of the robot’s steps according totheir subjective expectations.
4.4.5 Estimating thr
From the above findings, the person TE module uses
the number of steps from the robot to the trash items
to estimate thr . The total number of steps is calculated
from the total distance and total angle for the robot tofollow the route ζ .

Mitsuhiko Kimoto et al.

Let Dz be the distance and Φz be the rotation angle
along a line z that is a route between subgoals in ζ .
The number of steps forward, s, and the rotation, r, aregiven by
Dz
,
d
|Φz |
,
r=
φ
s=

(11)
(12)

where d is the distance that the robot can go forward
in one step, and φ is the rotation angle that it can turnin one step. Then, the total number of steps, S, can becalculated as
S = ∑ (s + r).

(13)

z∈ζ

Let zi be the route for the first subtask in theremaining path ζ . When the robot moves along zi ,we assume that there is no disruption in its actions.
The person TE module decrements the number of steps
from the initial value calculated by Equation (13). The
module recalculates the number of steps if it becomes
negative or the robot has to replan a new route becauseof an environmental change.
For instance, suppose that the person TE module
has to recalculate the route and the number of steps
when trash kicked by the robot rolls away from its
course. Let pr be the robot’s position, pt be the trashitem’s position, and θ be the robot’s rotation angle.
The person TE module judges that the robot kicked
the trash off course whendist(pr , pt ) · | sin(arctan(pt − pr ) − θ )| > robotwidth. (14)′ , which is the expected value of t , isHere, thrhrcalculated as follows:
tR
′
· S,
(15)
thr
=
S0

where tR is the time that the person expected before
starting the cooperative task, and S0 is the numberof steps calculated at the beginning of the task by
Equation (13). Because the person intuitively considers
the tR value, StR0 is related to her or his subjective
judgment.
′
4.4.6 Accuracy of Expected thr

We conducted another experiment with four
participants by using the same procedure described insubsection 4.4.6. In this case, however, we asked themto estimate the required number of steps instead of
the remaining time, according to the above finding on
how people estimate the time for the robot to finish
its task. The results indicated that the average errorHelp-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time′ and t for one participant
Fig. 7 Comparison between thr
hr

in the estimation was ±5.47 steps, and the correlation′ and t was 0.979.
between thr
hr
In addition, we verified whether the remaining time
can be estimated as well as the remaining numberof steps. We asked two participants to estimate therequired time thr . Figure 7 shows a graph that compares′ . The average error
the actual thr and the estimated thr
was ±22.22 s, and the correlation was 0.942. Thus,
because of its accuracy, we use Equation (15) as the
person’s criterion for estimating the progress of therobot’s task.

5 Experiment
We conducted an experiment to investigate
whether a robot that requested help by incorporating
an estimation of a person’s subjective time fortask completion would receive more help. We also
investigated whether this kind of help request gave abetter impression of the robot than a request that did
not consider the subjective remaining time estimatedfrom the person’s viewpoint.

9

estimate the remaining time for the task, the robot’s
actions were selected by the operator to improve its
behaviors for the task. Specifically, the operator madethe robot deal with the trash items in the order of red,green, yellow, and purple. The timing was based onwhen the robot went to put the next item in the trash
box after it had succeeded in putting the previous itemin the box. Success was determined by a ceiling camerathat recognized when a trash item was within the areaof the trash box.
The participant put the beans in the bowl one byone. The mass of beans was measured by an electricscale placed under the bowl, and this information was
sent to a computer. During the task, the robot asked
the participant to help by putting a trash item in thetrash box (e.g., “Excuse me. Would you put the purple
trash in the trash box?”). The participant then decided
whether to help the robot. If the participant decided tohelp the robot, she or he had to stop her or his own task
and help the robot. Otherwise, the participant could
ignore the robot’s request and continue the individualtask. When the ceiling camera recognized that a trashitem for which help was requested was within the trashbox, it was determined that the participant had helped
the robot.
To motivate the participants to help the robot, we
instructed them that the participant and the robot
needed to finish the task early by working together.
In addition, the participant and the robot were not tointervene in each other’s tasks except when the robot
asked for help; in other words, the participant was not
to help the robot unless the robot asked for help, even
if she or he had already finished the individual task.
5.2 Conditions

5.1 Setting
We used the same cooperative task and environmentshown in Figure 2. The human participant put beans ina bowl, while the robot put trash in a trash box. The
task was complete when all the trash items were in thetrash box and all the beans were in the bowl. There
were four trash items, colored red, green, yellow, andpurple, and 200 beans. During the task, at a time basedon the participant’s subjective estimation of the time tofinish it, the robot asked for help from the participant
to finish the task sooner.
The robot was operated by a human behind awall instead of by the route planning module. The
route planning module automatically generated a routeand replanned it according to the locations of thetrash items. Although the generated route was used to

We performed the experiment with the following
two conditions:Help-Estimator: The robot requested help by using theHelp-Estimator system to estimate both its own
required time and the person’s subjective estimatedtime.
NotPred: The robot requested help only by estimating
the required time; it did not consider whether the
person would accept a help request.
Thus, NotPred used only Equation (3) as thecriterion for generating a help request, while HelpEstimator used both Equations (3) and (4).We
compared the results under each condition toinvestigate whether predicting the person’s subjective
estimated time would increase the likelihood of
obtaining help from the person.

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Mitsuhiko Kimoto et al.

5.3 Participants

5.5 Measurements

We had 15 participants (12 men, 3 women; averageage 23.8 years, SD = 1.42) in the experiment. We used
a within-subject design and counterbalanced the orderof the conditions.

5.5.1 Objective Metrics

5.4 Procedure
Beforehand, an experimenter gave the participants
an outline of the experiment. They were informed thatthe goal was to finish both their task and the robot’s
task as quickly as possible. In addition, the participants
were asked to observe the robot’s motion and determine
its speed and amount of movement. This observation
was to make it easier for the participants to estimate
the time required for the robot’s task. The participants
were also told that the robot would request help during
the task. The experimenter told them how to handle therequest but did not explain the condition that would
prompt it.
Before starting the task, the experimenter asked
the participants to estimate the time that they andthe robot would require to perform their own tasks.
The participants estimated the time by assuming thatthey and the robot would perform their own tasks
independently of each other, without considering thecase of helping the robot. The estimated times wereused as tH in Equation (10) and tR in Equation (15).
After the participants had estimated these times, they
began the experiment.
During the task, the robot decided whether to ask
for help each time it received an action command
to move from the experimenter (every 4 seconds).
When the Help-Estimator system judged that the robot
should ask for help, it asked for help instead of moving.
The robot waited 10 seconds for the participant to
help; that is, it judged that it did not get help if theparticipant did not help within 10 seconds. When theparticipant did not help, the robot changed its status
to receive the next action command. In this case, therobot did not change the target trash item on which to
ask for help. Therefore, the robot sometimes asked forhelp on the same item several times according to the
judgment of Help-Estimator. Note that the robot did
not ask for help within 30 seconds of the task’s start.
The participants worked through two sessions foreach of the Help-Estimator and NotPred conditions.
After each session, they answered a questionnaire toevaluate their impressions of the robot. Then, at theend of the experiment, they were asked to choose whichrobot they would prefer to work with between the two
conditions.

To investigate the acceptability of the robot’s help
request, we measured a help ratio, which we calculatedby dividing the number of times that the participant
agreed to help and moved the trash to the trash box by
the number of times that the robot requested help.

5.5.2 Subjective Metrics
We used Godspeed [28] to measure the participants’
perception of the robot. Godspeed measures auser’s perception of a robot from the following five
perspectives: anthropomorphism, animacy, likability,
perceived intelligence, and perceived safety. Eachperspective includes multiple questions with fivepoint semantic differential scales, where one indicatesa negative perception and five indicates a positive
perception.
In addition, we used the following sevenquestionnaire items to evaluate the impressions of
the robot in the cooperative task and evaluated themon a seven-point scale ranging from 1 (disagree) to 7
(agree):– Did the robot try to reduce the time required forthe task?
– Did you rely on the robot?
– Did the robot depend on you?
– Did the robot make an effort?
– Was the robot cooperative?
– Was the robot friendly?
– Did the robot understand the situation of your task?
To evaluate the appropriateness of the timing of
the robot’s help requests, we used the following fivequestionnaire items and again evaluated them on aseven-point scale ranging from 1 (disagree) to 7 (agree).
– Did you feel reluctant when the robot asked forhelp?
– Did the robot ask for help in appropriate situations?
– Did you feel that the robot recognized the necessityof asking for help?
– Did you want to help the robot?
– Did the robot take your situation into account whenrequesting help?
Finally, as described above, we asked theparticipants to choose the robot they would prefer towork with between the two conditions.

Help-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time

Fig. 8 Help ratio

5.6 Hypotheses

H1: The help ratio would be increased by estimating
the person’s subjective estimated time.
The participants were expected not to help therobot when they were busy with their own task. We thus
expected that the help ratio would be higher for HelpEstimator than for NotPred, because the participants
had to pause their own task to have time to help therobot.
H2: The impression of the robot would be improvedby estimating the person’s subjective estimated time.
Because requesting help without accounting for the
person’s likelihood to help would increase the number oftimes the robot makes requests, frequent requests would
impair the person’s impression of the robot’s credibility.
We thus expected that Help-Estimator would keep thenumber of help requests to the minimum necessary sothat the robot would offer a better impression thanunder the NotPred condition.

6 Results
6.1 Objective Metrics
Figure 8 shows the help ratio results. We
conducted a paired t-test and found a significant
difference between Help-Estimator and NotPred(t(14) = 3.60, p = 0.003, d = 0.928) for the help ratio.

11

3.02, p = 0.009, d = 0.780), likability (t(14) = 4.24, p <0.001, d = 1.094), perceived intelligence (t(14) =4.30, p < 0.001, d = 1.110) and perceived safety (t(14) =2.23, p = 0.043, d = 0.576).
Figure 10 shows the results for the questions on
the impressions of the robot in the cooperative task.
We conducted a paired t-test and found a significant
difference between Help-Estimator and NotPred forthe following six items: “Did the robot try to reduce
the time required for the task?” (Help-Estimator >NotPred, t(14) = 3.06, p = 0.009, d = 0.789); “Did you
rely on the robot?” (Help-Estimator > NotPred, t(14) =3.02, p = 0.009, d = 0.780); “Did the robot make aneffort?” (Help-Estimator > NotPred, t(14) = 4.96, p <0.001, d = 1.282); “Was the robot cooperative?” (HelpEstimator > NotPred, t(14) = 4.08, p = 0.001, d =1.052); “Was the robot friendly?” (Help-Estimator >NotPred, t(14) = 3.33, p = 0.005, d = 0.860); and“Did the robot understand the situation of yourtask?” (Help-Estimator > NotPred, t(14) = 3.51, p =0.003, d = 0.907). We found no significant difference
between the two conditions for the other item: “Didthe robot depend on you?” (t(14) = 1.79, p = 0.095, d =0.463).
Figure 11 shows the results for the questions on
the appropriateness of the timing of the robot’s help
requests. We conducted a paired t-test and founda significant difference between Help-Estimator and
NotPred for all of the items: “Did you feel reluctant
when the robot asked for help?” (Help-Estimator <NotPred, t(14) = 2.94, p = 0.011, d = 0.759); “Did therobot ask for help in appropriate situations?” (HelpEstimator > NotPred, t(14) = 4.51, p < 0.001, d =1.166); “Did you feel that the robot recognized thenecessity for asking for help?” (Help-Estimator >NotPred, t(14) = 2.58, p = 0.022, d = 0.666); “Did you
want to help the robot?” (Help-Estimator > NotPred,t(14) = 4.96, p < 0.001, d = 1.281); and “Did therobot take your situation into account when requesting
help?” (Help-Estimator > NotPred, t(14) = 4.36, p <0.001, d = 1.126).
Finally, regarding the participants’ choice of whichrobot they would prefer to work with, 85% preferred
Help-Estimator, while the rest preferred the NotPred
robot.

6.2 Subjective Metrics

7 Discussion

Figure 9 shows the results for the Godspeedquestionnaire. We conducted a paired t-test and founda significant difference between Help-Estimator and
NotPred for all five perspectives: anthropomorphism(t(14) = 3.25, p = 0.006, d = 0.839), animacy (t(14) =7.1 Implications
The results shown in Figure 8 support hypothesis
H1. That is, estimating the person’s subjective
estimated time increases the probability of the robot

12

Mitsuhiko Kimoto et al.

Fig. 9 Mean scores for Godspeed items (1: negative; 5:
positive)

Fig. 11 Mean scores for questionnaire items on theappropriateness of the timing of the robot’s help requests (1:disagree; 7: agree)

such as “I felt that the robot tried to finish the task
earlier” and “the robot requested help while checking
on my situation.” Although 15% of the participantspreferred the NotPred robot, even they always helped
the robot. Some of them said, “I couldn’t understandthe difference between Help-Estimator and NotPred.”

7.2 Effects of Other Possible Factors
Fig. 10 Mean scores for questionnaire items on the impressionsof the robot (1: disagree; 7: agree)

receiving help. Regarding why people did not help therobot, the reasons included not only responses such as“my task takes time” but also “I want the robot to
try harder.” Figure 10 shows evidence similar to thesecomments, indicating that the NotPred robot put inless effort than the Help-Estimator robot. Also, from
Figure 11, the participants felt that the Help-Estimator
robot requested help at a more appropriate time thanthe NotPred robot, and they preferred to help the robot
equipped with Help-Estimator. We can conclude thatthe help ratio will increase if the robot works hard tosome extent, rather than asking for help more often
than necessary.
The results shown in Figures 9, 10, and 11
indicate that the Help-Estimator robot offered better
impressions than the NotPred robot, which supports
hypothesis H2. The better impression is also shown by
the 85% of the participants who preferred the HelpEstimator robot. The reasons for this included opinions

In the experiment, we only focused on the person’s
subjective time estimation for the task. However, otherinternal factors such as expertise, trust, motivation,and engagement also could affect people’s decisions onwhether to help. To understand the effects of thesefactors, two coders categorized the answers from afree-description questionnaire on why the participants
did or did not help the robot into the following
five primary types: (1) subjective time estimation for
the task, (2) robot’s attitude, (3) robot’s capability,(4) number of requests, and (5) other. The coding
procedure was as follows. First, the coders categorized
the descriptive feedback into the five types. Next, to test
the interrater reliability we calculated Cohen’s kappa;
it was 0.679, indicating substantial reproducibility.
Finally, the coders discussed the discrepancies in theircategorizations and categorized the answers.
As a result, the percentages of categorized feedback
were as follows: (1) subjective time estimation for thetask, 63.8%; (2) robot’s attitude, 10.3%; (3) robot’s
capability, 13.8%; (4) number of requests, 10.3%; and(5) other, 1.7%. First, there was much feedback on(1) the subjective time estimation for the task: e.g.,Help-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Time“I thought if I helped the robot, we would finish
the task more quickly,” and “I didn’t help the robot
because my task didn’t seem like it would end any
time soon.” This result indicates that the subjective
time estimation was an important factor when theparticipants considered the robot’s help requests. It also
supports the experimental result that estimating theperson’s subjective time increased the probability of therobot receiving help.
There was also feedback related to the other factors.
Some participants remarked on (2) the robot’s attitude:e.g., “I helped the robot because it asked me to help
after trying its best,” and “I didn’t help the robot
because it didn’t seem to be trying to do the task byitself.” This result suggests that, in order to get more
help from people, it is important to design a robot’s
behaviors so that it appears to be making an effort.
This mental factor should be important in the context
of human collaboration preferences, and we will needto investigate its effects in a future work. In addition,
there were remarks on (3) the robot’s capability: e.g.,“I helped the robot because the trash was behind therobot and it was diﬀicult for the robot to collect it,”and “The trash was at the robot’s feet and I thought
it could collect the trash by itself.” Although these
remarks are diﬀicult to clearly distinguish from thefeedback on the subjective time estimation for the task,
they suggest the effect of people’s preconception of
the robot’s capability. We will also need to further
investigate this point in the future. Next, there was
feedback on (4) the number of help requests: e.g., “I
helped the robot because it only asked me to help a
few times,” and “Once, I tried to ignore the help request
because of my task situation, but the robot asked a few
times and I reluctantly helped it.” This type of feedback
suggests a simple relationship whereby, if the robot asks
for help many times, people will agree to help. On theother hand, this type of feedback sometimes indicateda negative impression, meaning that the robot should
avoid frequent requests. Finally, the (5) other category
included one remark: “I just wanted to help the robot.”
7.3 Effects on Required Time to Finish Task
In the experiment, we did not compare the robot in
the Help-Estimator and NotPred conditions to a robotthat did not ask for help at all. To investigate whether
and by how much the help requests reduced the required
time to finish the task, we additionally gathered datafrom an experiment with five participants, in which
the robot did not ask for help. Figure 12 shows themean time required to finish the task when the robot
did not ask for help, along with the times from the13

Fig. 12 Mean time to finish the task when the robot did not
ask for help and in the Help-Estimator and NotPred conditions

original experiment in the Help-Estimator and NotPred
conditions. The results suggest that the robot’s help
requests reduced the time to finish the task, while thetime differed little between the Help-Estimator and
NotPred conditions. In our experimental setting, anincreased rate of agreeing to help requests was notdirectly related to a decrease in the time to finish thetask. We used a simple task to focus on the effects
of incorporating a human’s subjective view of her or
his own task when a robot asks for help. In a more
complicated or time-consuming situation, the rate ofagreeing to help requests may affect the time to finish
the task. Further research will be needed on what kindsof cooperative tasks will show eﬀicient reductions in therequired time when help requests consider a person’s
subjective time estimation.

7.4 Limitations
In this study, we used a simple artificial task inthe preliminary case study and the experiment. We
designed the simple task to enable the robot to estimate
the remaining time for its task; we also assumed thatthe participants would easily estimate the remaining
time for their task. In more complex or long-term tasks
in which it is diﬀicult for people to estimate theirprogress, they would behave in different ways fromthis study. Therefore, this study suffers from some
limitations, and it is diﬀicult to generalize its results
to other tasks. Furthermore, Help-Estimator was basedon several assumptions; that is, the robot assessed
whether to ask for help at a 4-second frequency, andit did not ask for help for 30 seconds from the start of
the task. Therefore, the applicability and generality of
Help-Estimator in other domains is also limited.
The method of estimating the required time inthis experiment was specific to the cleaning task.
It will be necessary to develop other models of

14estimating the required time for the robot’s task andthe estimated time for the person’s task when applying
Help-Estimator to other cooperative tasks. In addition,
the task here had to be one whose required time asa system could be quantitatively estimated in order
to measure the time required for each task. However,people and robots sometimes engage in cooperative
tasks that cannot be quantitatively estimated. Further
research will be necessary to estimate the robot’s time
and the person’s time even for such nonquantitative
tasks.
We also focused only on the person’s subjective
time estimation according to our observations in the
preliminary case study. Therefore, it is unknown
whether other human factors, such as expertise, mood,trust, or engagement, could be effective for increasinga person’s willingness to help a robot. To incorporatesuch factors, we will need to extend the case study andobtain further observations.
For the robot to estimate people’s subjective
estimated time for their task, Help-Estimator requires
asking them to estimate the required time before
starting the cooperative task. In the real world,
however, it would be diﬀicult to apply this style ofinitially setting multiple tasks, and it would not berealistic to ask a person the required time for a task.
Thus, Help-Estimator should dynamically estimate thelikelihood of a person helping through the interaction
between the person and the robot.
In the experiment, the participants were instructed
to work together with the robot to finish the task early,
so they may have been biased toward being inclined tohelp. However, they followed the instructions during
both experimental conditions (Help-Estimator and
NotPred), and they were influenced by this bias at
the same level under both conditions. In addition,
the order of the conditions was counterbalanced. In
this study, we only controlled the factor of whether
the robot considered the participant’s subjective time
estimation when it asked for help. Therefore, otherinternal factors were balanced between the conditions,and the experiment was a reasonable one to verify
the effect of Help-Estimator. On the other hand, toinvestigate the effects of other internal factors, theinstructions will need to be redesigned.
8 Conclusion
In this paper, we focused on a person’s subjective
estimation of the time required for her or his individualtask as part of a cooperative task with a robot. Our
experimental results showed that the robot could obtain
more help from the participants and was preferred by

Mitsuhiko Kimoto et al.

them when it asked for help by considering the person’s subjective time estimation.
In our future work, we will need to improve theaccuracy of the robot’s prediction of the estimatedtime in order to receive more help from people. In
addition, the Help-Estimator system should update theestimation when a person does not help the robot.
We should also reduce the estimation error of HelpEstimator through more interactions with people.
Funding This work was supported by JSPS KAKENHI
Grant Number JP26118006 and JST CREST Grant Number
JPMJCR19A1.
Conflict of interest The authors declare that they have noconflict of interest.

References
1. Satake S, Hayashi K, Nakatani K, Kanda T (2015) Field
trial of an information-providing robot in a shopping mall.
In: 2015 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), pp 1832–1839, DOI 10.1109/
IROS.2015.7353616
2. Burgard W, Cremers AB, Fox D, Hähnel D, Lakemeyer G,
Schulz D, Steiner W, Thrun S (1999) Experiences with aninteractive museum tour-guide robot. Artificial Intelligence
114(1):3–55, DOI https://doi.org/10.1016/S0004-3702(99)00070-3
3. Shiomi M, Kanda T, Ishiguro H, Hagita N (2006)
Interactive humanoid robots for a science museum. In:
Proceedings of the 1st ACM SIGCHI/SIGART Conferenceon Human-robot Interaction, ACM, New York, NY, USA,
HRI ’06, pp 305–312, DOI 10.1145/1121241.1121293, URL
http://doi.acm.org/10.1145/1121241.1121293
4. Joosse M, Evers V (2017) A guide robot at the airport:
First impressions. In: Proceedings of the Companion of
the 2017 ACM/IEEE International Conference on HumanRobot Interaction, HRI ’17, pp 149–150
5. Hoffman G (2013) Evaluating fluency in human-robot
collaboration. In: International conference on human-robot
interaction (HRI), workshop on human robot collaboration,
vol 381, pp 1–8
6. Baraglia J, Cakmak M, Nagai Y, Rao R, Asada M (2016)
Initiative in robot assistance during collaborative task
execution. In: Human-Robot Interaction (HRI), 2016 11th
ACM/IEEE International Conference on, IEEE, pp 67–74
7. Huang CM, Mutlu B (2016) Anticipatory robot control
for eﬀicient human-robot collaboration. In: Human-Robot
Interaction (HRI), 2016 11th ACM/IEEE International
Conference on, IEEE, pp 83–90
8. Shah J, Wiken J, Williams B, Breazeal C (2011) Improved
human-robot team performance using chaski, a humaninspired plan execution system. In: Proceedings of the
6th international conference on Human-robot interaction,
ACM, pp 29–36
9. Knepper RA, Tellex S, Li A, Roy N, Rus D (2015)
Recovering from failure by asking for help. Autonomous
Robots 39(3):347–362
10. Fischer K, Soto B, Pantofaru C, Takayama L (2014)
Initiating interactions in order to get help: Effects

Help-Estimator: Robot Requests for Help from Humans by Estimating a Person’s Subjective Timeof social framing on people’s responses to robots’requests for assistance. In: Robot and Human Interactive
Communication, 2014 RO-MAN: The 23rd IEEE
International Symposium on, IEEE, pp 999–1005
11. Maeda GJ, Neumann G, Ewerton M, Lioutikov R, Kroemer
O, Peters J (2017) Probabilistic movement primitives for
coordination of multiple human–robot collaborative tasks.
Autonomous Robots 41(3):593–612
12. Dragan AD, Bauman S, Forlizzi J, Srinivasa SS (2015)
Effects of robot motion on human-robot collaboration. In:
Proceedings of the Tenth Annual ACM/IEEE International
Conference on Human-Robot Interaction, ACM, pp 51–58
13. Maniadakis M, Hourdakis E, Trahanias P (2017) Timeinformed task planning in multi-agent collaboration.
Cognitive Systems Research 43:291–300
14. Nikolaidis S, Ramakrishnan R, Gu K, Shah J (2015)
Eﬀicient model learning from joint-action demonstrations
for human-robot collaborative tasks. In: Proceedings of
the Tenth Annual ACM/IEEE International Conference on
Human-Robot Interaction, ACM, pp 189–196
15. Wang N, Pynadath DV, Hill SG (2016) Trust calibration
within a human-robot team: Comparing automaticallygenerated explanations. In: The Eleventh ACM/IEEEInternational Conference on Human Robot Interaction,
IEEE Press, pp 109–116
16. Clodic A, Cao H, Alili S, Montreuil V, Alami R, Chatila
R (2009) Shary: a supervision system adapted to humanrobot interaction. In: Experimental Robotics, Springer, pp
229–238
17. Weiss A, Igelsböck J, Tscheligi M, Bauer A, Kühnlenz
K, Wollherr D, Buss M (2010) Robots asking for
directions: the willingness of passers-by to support robots.
In: Proceedings of the 5th ACM/IEEE international
conference on Human-robot interaction, IEEE Press, pp
23–30
18. Cha E, Matarić M (2016) Using nonverbal signals
to request help during human-robot collaboration. In:
Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ
International Conference on, IEEE, pp 5070–5076
19. Srinivasan V, Takayama L (2016) Help me please: Robot
politeness strategies for soliciting help from humans. In:
Proceedings of the 2016 CHI Conference on Human Factors
in Computing Systems, ACM, pp 4945–4955
20. Cai H, Mostofi Y (2015) To ask or not to ask: A foundation
for the optimization of human-robot collaborations. In:
American Control Conference (ACC), 2015, IEEE, pp 440–
446
21. Bajones M, Weiss A, Vincze M (2016) Help, anyone?
a user study for modeling robotic behavior to mitigate
malfunctions with the help of the user. arXiv preprint
arXiv:160602547
22. Yasumatsu Y, Sono T, Hasegawa K, Imai M (2017) I
can help you: Altruistic behaviors from children towards arobot at a kindergarten. In: Proceedings of the Companionof the 2017 ACM/IEEE International Conference on
Human-Robot Interaction, ACM, pp 331–332
23. Hayes B, Ullman D, Alexander E, Bank C, Scassellati
B (2014) People help robots who help others, notrobots who help themselves. In: Robot and Human
Interactive Communication, 2014 RO-MAN: The 23rdIEEE International Symposium on, IEEE, pp 255–260
24. Cameron D, Collins EC, Chua A, Fernando S, McAree
O, Martinez-Hernandez U, Aitken JM, Boorman L, Law J(2015) Help! i can’t reach the buttons: Facilitating helping
behaviors towards robots. In: Biomimetic and Biohybrid
Systems, Springer International Publishing, pp 354–358

15

25. Mirnig N, Stollnberger G, Miksch M, Stadler S, Giuliani M,
Tscheligi M (2017) To err is robot: How humans assess andact toward an erroneous social robot. Frontiers in Robotics
and AI 4:21
26. Grondin S (2010) Timing and time perception: areview of recent behavioral and neuroscience findings
and theoretical directions. Attention, Perception, &Psychophysics 72(3):561–582
27. Maniadakis M, Droit-Volet S, Trahanias P (2017)
Emotionally modulated time perception for prioritizedrobot assistance. In: Proceedings of the Companion of
the 2017 ACM/IEEE International Conference on HumanRobot Interaction, ACM, pp 197–198
28. Bartneck C, Kulić D, Croft E, Zoghbi S (2009)
Measurement instruments for the anthropomorphism,
animacy, likeability, perceived intelligence, and perceived
safety of robots. International journal of social robotics
1(1):71–81