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How May I Serve You? A Robot Companion Approaching a
Seated Person in a Helping Context
K. Dautenhahn, M. Walters,
S. Woods, K. L. Koay, C. L. Nehaniv,
University of Hertfordshire,
College Lane,
Hatfield, UK, AL10 9AB
+44 (0) 1707 285113
K.Dautenhahn@herts.ac.uk
E. A. Sisbot, R. Alami,
T.Siméon
LAAS-CNRS
7, Avenue de Colonel Roche,
31077 Toulouse, FRANCE
+33 (0) 5 61 33 63 46
Emrah.Akin.Sisbot@laas.fr
ABSTRACT
This paper presents the combined results of two studies that
investigated how a robot should best approach and place itself
relative to a seated human subject. Two live Human Robot
Interaction (HRI) trials were performed involving a robot fetching
an object that the human had requested, using different approach
directions. Results of the trials indicated that most subjects disliked
a frontal approach, except for a small minority of females, and
most subjects preferred to be approached from either the left or
right side, with a small overall preference for a right approach by
the robot. Handedness and occupation were not related to these
preferences. We discuss the results of the user studies in the context
of developing a path planning system for a mobile robot.
Categories and Subject Descriptors
A.m [Miscellaneous]: Human Robot Interaction – Social Robots
I.2.9 [Artificial Intelligence]: Robotics – Mobile robots
General Terms
Human Factors,
Keywords
Human-robot interaction, social robot, social spaces, personal
spaces, user trials, live interactions
1. INTRODUCTION
If robots are to be used in office and domestic environments, they
will have to encounter and interact with people. They must survive
and carry out tasks in a disordered and unpredictable environment,
safely and effectively. This paper presents the results from Human
Robot Interaction (HRI) trials carried out at the University of
Hertfordshire (UH). These results have then been used to inform
and guide work carried out at the Laboratory for Analysis and
Architecture of Systems at the Centre National de la Recherche
Scientifique (LAAS-CNRS), to develop a task planning, motion
planning, and control system that incorporates human social factors
and preferences.
The work presented in this paper contributes to the COGNIRON
Project [2005]. Part of this research into a cognitive robot
companion investigates socially interactive robots [7] from a
human-centred perspective, i.e. how robots could be useful in
domestic environments; in particular the roles, tasks, and social
behaviour(s) that will be necessary for robots to exhibit in order to
integrate into everyday domestic situations. In order to study
human-robot relationships, HRI trials using carefully devised test
scenarios are conducted [18], where human responses and opinions
can be collected using a variety of methods. A number of previous
live HRI trials with human scaled PeopleBotTM robots have been
carried out [6, 17, 19, 20]. Other researchers have also investigated
similar HRI trials with human sized robots including Dario et al.
[4], Severinson-Eklundh et al. [16], Kanda et al. [9] and Hinds et
al. [8].
Once the desired behaviour(s) for sociable robots capable of
competent human-robot interactions are known, the challenge is
then to incorporate the results into mobile robot path planning
algorithms and control systems. At LAAS-CNRS progress has
been made towards a motion planning framework that will allow
the implementation of key criteria and parameters that can
incorporate these results into the control system of a mobile robot
that can be applied to human-centred environments. The presence
of humans raises new issues for motion planning and control since
the human’s safety and comfort must be taken into account. The
claim here is that a human-aware motion planner must not only
consider safe robot paths, but also plan good, socially acceptable
and legible paths.
There are a number of contributions in the literature where humans
and robots co-exist in the same environment. These studies have
frequently focussed only on the safety of the human [2, 10, 11, 21]
and have failed to take human comfort into account. The planner
presented here explicitly takes into account the human partners’
safety and comfort by reasoning about accessibility, visual field,
posture, gaze direction, relative distance to the robot and potential
shared motions. Although several authors have proposed motion
planning or reactive schemes with a consideration for humans,
there is no contribution that has tackled this whole problem.
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2. The Live HRI Trials
This section presents results from two live HRI trials. First, a
human-robot interaction demonstration trial event, which was run
as part of an informal evening event at the AISB’05 Convention
held at University of Hertfordshire in April 2005, and secondly,
follow-up trials carried out in a controlled laboratory set-up, to re-
test the results gained from the demonstration trial.
2.1 The HRI Trial Method
The trials were both carried out in converted seminar rooms where
the scenario involved a robot using three different approach
directions (front, left and right) to bring a seated subject an object
(a TV remote control). The main aim of both trials was to
establish subjects’ preferences for the different robot approach
directions. The demonstration event was conducted as part of an
evening of entertainment for convention delegates, and involved
different robot demonstrations. Spectators were present during the
trials which were performed under non-laboratory conditions using
38 volunteers from the convention. The follow up study was
carried out under controlled conditions with 15 subjects, and one of
the main aims of this trial was to re-test the results obtained from
the informal study.
2.1.1 The Trial Areas
The trial set-up was virtually identical for both trials and resembled
a simulated living room with a chair and two tables. The subject
was seated in the chair, which was positioned halfway along the
rear wall (point (9), Fig.1), throughout the trial. To the left front,
and right front of the chair, two tables were arranged (with room
for the robot to pass by) in front of the chair. One of the tables had
a television placed upon it; the other had a CD Radio unit. The
robot was driven under direct remote control to the appropriate
start position by an operator, but the robot’s approaches to the
subject were fully autonomous. The operator was seated at a table
in the far corner of the room. Subjects were told that the robot
would be controlled by the operator while it was driven to the three
start positions, but would be approaching them autonomously to
bring them the TV remote control. This was reinforced as the
operator made notes and did not press any of the robot control keys
(on the robot control laptop) while it approached the subject
(Figure 1). The robot carried the remote control in a small basket
suspended between the fingers of the lifting gripper. The remote
control was placed in the basket prior to each experimental run. For
each approach trial, the subject took the remote from the basket
then replaced it ready for the next approach.
2.1.2 The HRI Trial Scenario
The same scenario was used for both HRI trials, introduced by the
experiment supervisor. The context explained to the subjects was
as follows: the subject had arrived home, tired after a long day at
work and rested in an armchair (point (9), Fig.1). After looking
around for the TV remote control, the subject then asked the robot
to fetch it for them as they were too tired to get up. The robot then
brought the remote control to the subject. It was explained to the
subject that the robot was new to the household and it was
necessary to find out which approach direction the subject
preferred; either from the front (2), the left (1) or the right (3). The
three possible paths taken by the robot are shown in Fig. 1. In
order to justify the scenario of the robot fetching the remote
control, one of the tables had a (switched off) TV set upon it. The
other table had a CD-Radio unit. Our expectations prior to the
trials were that subjects would prefer the approach from the front,
since the robot was then fully visible at all times. Since many
subjects, in particular in the demonstration trial, had never seen the
robot before we assumed that they would feel most secure,
comfortable, and ‘in control’ when the robot was fully visible so
that its behaviour could be monitored easily.
Armc ha ir
W OZ
Op era tor
Sea t
Ta ble w ith
Ra dio /C D
Table
with TV
Ro bot A ppr oach Path . Sp eed = 0.4 m/s
Ro bot A ppr oach Path . Sp eed = 0.2 5 m/s
L ive Tri al A rea
820
1145
Figure 1. Live Trial Area
Figure 2. Examples of the demonstration HRI trial
Figure 3. Example of the follow-up HRI trial.
2.1.3 Experimental Conditions
We were aware that the TV might be a natural focus of subjects’
attention and may have influenced the choice of preferred robot
approach direction. Therefore, for the controlled lab condition, half
the trials were carried out with the TV on the left hand table, and
the other half with the TV on the right hand table. Each subject
experienced the robot approaching from three directions: front, left
and right. To avoid any order effects, a counterbalanced order
sequence covering all six possible permutations of the three robot
approach directions was used. For the demonstration event,
subjects experienced each approach direction only once, and for the
controlled follow-up trials, each subject experienced the three robot
approach directions twice, in a counterbalanced order.
2.1.4 Subject Sample Sets:
For the demonstration trial, 21 males (54%) and 18 females (46%)
participated. The mean age of subjects was 36 years (range: 22-
58). Thirty five subjects (95%) were right handed, and 2 subjects
(5%) were left handed. All were delegates at the AISB’05
Convention. Fifteen subjects (9 (60%) males; 6 (40%) females)
participated in the follow-up study. The mean age of this sample
was 33 years (range 21-56 yrs). Only one subject was left handed.
Four subjects were secretarial staff, 5 subjects were MSc students
studying ‘Artificial Intelligence’, and the remaining 6 were research
staff in the Computer Science Department at University of
Hertfordshire. No subjects had previous exposure to the robots
used in the trial. In the demonstration trial, some subjects had not
sat straight in the chair (see Fig. 2). In the follow-up study subjects
were made to sit straight with their feet to the front of the chair.
2.1.5 Procedure
For both trials, subjects completed a short introductory
questionnaire to gain the necessary consent, and demographic
details. At the end of each trial a semi-structured questionnaire
was used to assess subject attitudes and preferences for the
different robot approach directions and approach speed, as well as
practicality issues. The questionnaires used for the follow-up trials
were more extensive and included questions about the robot
stopping distances, comfort levels and practicality for the different
approach directions, rated according to a 5-point Likert scale.
Subjects also participated in a semi-structured interview after the
follow-up trial. The interview was carefully designed to eliminate
leading questions. The main purpose of the structured interview
was to assess subjects’ views about the trial procedures and
methodology, and find out how the trial could be improved from
the participants’ point of view. The subjects’ reactions to both HRI
trials were recorded by a single tripod mounted camera placed at an
appropriate point at either (5) or (6) in Fig. 1.
2.2 Demonstration Trial Results
2.2.1 Overall Approach Direction Preferences:
0
20
40
60
80
100
prefer least prefer
Approach Direction
Percenta g e
Front
Left
Right
Figure 4. Demonstration trial: Robot to human approach
direction preferences.
Figure 4 illustrates that 60% (N: 23) of subjects stated that they
preferred the right robot approach direction, followed by 24% (N:
9) preferring the left approach and just 16% (N: 6) preferring the
front approach. An overriding majority of subjects stated that they
least preferred the frontal robot approach direction (N: 31, 80%).
Very few subjects least preferred the left and right approach
directions.
2.2.2 Gender Differences & Approach Direction
Preferences
.
0
20
40
60
80
front left right
Approach Direction
P e r c e n ta g e
male
female
Figure 5. Male and female approach direction preferences
Chi-square cross-tabulations revealed a significant trend between
gender and the preferred robot approach direction (X2 (2, 38) =
3.77, p = 0.1). More females stated that they preferred the front
robot approach direction compared to males, and more males
preferred the right robot approach direction compared to females
(see Figure 5). A significant relationship was found between
gender and least preferred robot approach direction (X2 (2, 39) =
7.09, p = 0.03). Significantly more males stated that they least
preferred the front robot approach direction compared to females
(males: 95%, females: 61%). More females stated that they least
preferred the right robot approach direction compared to males
(males: 0%, females: 11%).
2.2.3 Age, Handedness, and Approach Direction
Preferences
Chi-square cross-tabulations revealed no significant relationships
between age, handedness and approach directions preferred and
least preferred.
2.2.4 Approach Distance
76% (N: 28) of subjects stated that the distance between them and
the robot (0.5m ±0.1m) was ‘about right’, followed by 19% (N: 7)
who felt that the robot was to ‘too far’ from them. Only 5% (N: 2)
of subjects stated that the robot approached them too closely.
2.2.5 Practicality of Approach Directions
In addition to subjects rating which robot approach direction they
preferred, ratings were given for how ‘practical’ they thought each
approach direction was for the given task of delivering a TV
remote control, according to a 5-point Likert scale (1 = not
practical at all to 5 = very practical). A Friedman test for ordinal
data illustrated that the rankings for approach direction practicality
were significantly different from each other (X2 (39, 2) = 12.11, p <
0.01). The mean rankings indicated that the front approach
direction (mean ranking = 1.63) was rated as the least practical,
and the right approach the most practical (mean ranking = 2.33),
followed by the left (mean ranking = 2.04) approach direction.
2.2.6 Comfort Ratings of Approach Directions
Subjects were asked to rate how comfortable they felt with the
different robot approach directions trials according to a 5-point
Likert scale (1 = very uncomfortable, 5 = very comfortable). A
Friedman test showed that the comfort level rankings for approach
directions were significantly different from each other (X2 (39, 2) =
29.38, p < 0.001). The mean rankings highlighted that subjects
were the least comfortable with the front (mean ranking = 1.37)
robot approach direction, and the most comfortable with the right
approach direction (mean ranking = 2.49), followed by the left
(mean ranking = 2.14).
2.3 Follow-Up Trial Results
2.3.1 Approach directions most and least preferred
0
20
40
60
80
100
prefer least prefer
Approach Direction
Percentage
Front
Left
Right
Figure 6. Follow-up trial: Least preferred and most preferred
robot to human approach directions.
Results of the follow-up approach direction robot trials under
laboratory conditions clearly demonstrated that the least preferred
approach direction was the front approach. The right approach
direction was the most preferred. These results are highly
consistent, with the demonstration trial results (Figure 6).
2.3.2 Robot Distance from the Subject
For the robot’s front approach direction stopping distance, 53% (N
= 8) of subjects rated that the robot’s stopping distance was too
close. 27% (N = 4) of subjects rated that the robot’s stopping
distance was about right, and 20% rated that robot’s stopping
distance was too far. These results seem to indicate that a near
majority of subjects rated that the front approach stopping distance
was too close. In the case of subjects who rated the stopping
distance as being too far for the front approach, we observed that
these subjects usually had their legs stretched out in front of them
causing the robot to stop when it reached the subject’s feet rather
than their arm for them to reach the TV remote control (due to the
robot’s stopping safety mechanism which had to be operational due
to safety considerations). During the robot’s approach from the
left direction, 80% (N = 12) stated that the stopping distance was
about right and 20% (N = 3) rated the stopping distance as being
too far. During the robot’s approach from the right of the subject
60% (N = 9) of subjects rated the stopping distance as about right,
and 40% (N = 6) rated it as too far. It is interesting to note that no
subjects thought the robot approached too closely from either left
or right approach directions.
2.3.3 Robot’s Speed during the Trial
The robots final approach speed to the subject was approximately
0.4 to 0.25 m/s, but was not finely controlled due to the inbuilt
safety speed limiting mechanism. When subjects were asked to
rate the robot’s approach speed, 60% (N: 7) of participants rated
that the speed was about right, and 40% (N: 6) of subjects rated
that the robot’s speed was too slow. None of the subjects rated that
the robot’s speed was too fast during the trials.
2.3.4 Practicality and Comfort of the different
Robot Approach Directions
The front approach direction received the lowest practicality ratings
for both the live and video trials. The right approach direction
received the highest ratings of practicality followed by the left
approach. The lowest mean comfort levels were found for the front
robot approach direction. The highest comfort level rating was
found for the right approach direction followed by the left approach
direction. No significant differences were found between most
preferred approach direction and least preferred approach direction
for gender, subject handedness (whether subject was left or right
handed), and occupation.
2.4 Combined Results of Demonstration &
Follow-Up Trials
In light of the comparable HRI trial methodologies and the high
degree of agreement between the results from the informal
demonstration trials and formal follow-up trials, the results from
both trials were combined to form one dataset from the 55 subjects
who participated in both trials. Thirty males (56%) and 24 females
(44%) in total participated in the robot approach direction trials.
The mean age of subjects was 36 years (range: 21-58, SD: 11.54).
Forty nine subjects (94%) were right handed, and 3 subjects (6%)
were left handed.
2.4.1 Trial Preferences:
0
20
40
60
80
100
prefer least prefer
Approach Direction
Pe r ce nt ag e
Front
Left
Right
Figure 7. Combined trial results: Overall robot to human
approach direction preferences
Figure 7 illustrates that 59% (N: 31) of subjects stated preferring
the right robot approach direction, followed by 28% (N: 15) who
preferred the left approach, and just 13% (N: 7) preferred the front
approach. An overriding majority of subjects stated least preferring
the front robot approach direction (N: 43, 80%). Few subjects
least preferred the left and right approach directions.
2.4.2 Practicality of Approach Directions
A Friedman test for ordinal data illustrated that the rankings for
approach direction practicality were significantly different from
each other (X2 (54, 2) = 21.87, p < 0.001). The mean rankings
indicate that the front approach direction (mean ranking = 1.55)
was rated as the least practical, and that the right approach was the
most practical (mean ranking = 2.34), followed by the left (mean
ranking = 2.11) approach direction.
2.4.3 Comfort Ratings of the Approach Directions
Results from a Friedman test showed that the comfort level
rankings for approach directions were significantly different from
each other (X2 (54, 2) = 47.78, p < 0.001). The mean rankings
highlight that subjects were the least comfortable with the front
(mean ranking = 2.43) robot approach direction, and the most
comfortable with the right approach direction (mean ranking =
4.15), followed by the left (mean ranking = 3.76).
2.4.4 Gender Differences
Chi-square cross-tabulations revealed a significant association
between gender and the robot approach direction preferred (X2 (2,
53) = 5.83, p = 0.05). More females stated preferring the robot
front approach direction compared to males, and more males
preferred the right robot approach direction compared to females
(See Figure 8). A small significant relationship was found between
gender and least preferred robot approach direction (X2 (2, 54) =
5.72, p = 0.06). More males stated least preferring the front robot
approach direction compared to females (males: 90%, females:
67%). More females stated least preferring the left (males: 10%,
females: 21%) and right robot approach direction compared to
males (males: 0%, females: 13%). Independent measures t-tests
revealed a trend for males (M = 4.37) to rate the right robot
approach direction as more comfortable compared to females (M =
3.88) (t (52) = 1.74, p = 0.08). No further significant gender
differences were revealed for comfort ratings of the front and left
robot approach directions.
0
20
40
60
80
front left right
Approach Direction
Percentage
male
female
Figure 8. Combined results: Male and female preferences
Independent measures t-tests were calculated to examine gender
differences and ratings of the practicality of the robot approach
directions. Significant differences were found for the practicality
of the front approach direction (t (52) = -2.46, p = 0.02). Females
rated the front approach direction as significantly more practical
compared to males (males M = 2.60, females M = 3.38). No
further significant differences were found between gender and
practicality ratings for the left and right approach directions.
2.4.5 Age, Handedness, and Approach Direction
Preferences
Chi-square cross-tabulations revealed no significant relationships
between age, handedness, approach directions most and least
preferred, comfort ratings of the approach directions, and
practicality ratings of the approach directions.
2.4.6 Comments made by Subjects about the Three
Robot Approach Directions.
Subjects were asked to provide details about the reasons for
preferring and least preferring particular robot approach directions.
The most frequently cited comments are provided in tables 1 and
2.1
Table 1. Reasons why subjects preferred a particular
approach direction.
Preferred Front Approach Direction
Front approach direction was easy to reach for the TV remote control
The effort needed to reach for the remote control was the least, but
this was still not close enough
Preferred Left Approach Direction
I felt the most relaxed and comfortable during this approach
Preferred this approach as I am left handed
This approach was the quickest and most direct
This approach felt the most natural
It was the most convenient for the robot to approach this way.
Preferred Right Approach Direction
I felt the most comfortable with this approach
This approach seemed the most natural
I am right handed, so it was the easiest way to take the remote
control
This approach seemed to be the quickest
This approach because it was always in my field of vision
Table 2. Reasons why subjects least preferred a particular
approach direction.
Least Preferred Front Approach Direction
I had to move forward to reach for the remote control, the robot was
too far away from me
This approach was slightly threatening
This approach was just a little bit too close for comfort
Seemed too aggressive
The robot was always looking a me
I was concerned about the robot running into me during this
approach
This approach was intimidating
Least Preferred Left Approach Direction
Didn’t like left approach as I am right handed
It was difficult for me to reach for the remote control
I felt awkward reaching across with me left hand
It felt like I had to reach further for the left approach
The robot was not in my line of vision during the left approach
Least Preferred Right Approach Direction
Least preferred this approach because I am left handed
The robot felt like it was behind my back during this approach
Implications of User Studies for Robot Motion
Planning
Today, classical motion planning methods [12] are quite efficient at
locating feasible paths. However, the presence of humans in the
environment drastically changes the notion of acceptable paths. In a
human-robot interaction context, the computed paths do not only
need to be collision-free but must also take into account human
1 Due to space limitations, only the most frequently cited
comments are shown.
comfort. This is illustrated in figure 9, which shows two paths
produced by a classical motion planner. Both paths are
inconvenient since one path passes too close to the wall, causing
the human to be surprised, and the other passes behind the human
resulting in discomfort. The HRI studies reported in the previous
section, and others [1, 13, and 19] highlight a number of properties
that must be taken into account when dealing with humans. Only
limited studies have considered comfort and legibility issues, often
in an ad hoc manner. A new technique is described that integrates
additional constraints in a more generic way. In these steps of our
work, we assume that the final positions of the paths are already
calculated.
Figure 9. Two paths found by classical motion planning
systems
We introduce three criteria to the motion planning stage to ensure
safety and comfort. The robot must take into account these three
criteria at the planning stage along with the more common aspects
of path planning such as obstacle avoidance. Each criterion is
represented by human-centred costs stored in a 2D grid:
Safety Criterion: This focuses on ensuring safety by controlling
the distance between the robot and human. The robot, if possible,
must avoid approaching the human too closely, and in some cases
(i.e. no physical interaction) the robot must not be able to pass
through a certain perimeter around the human. However, the robot
must be able to approach the human to allow interactions to occur
(for example to pass an object to a human). Hence, this distance
between the robot and the human is not uniform and fixed, but
depends on the type of human-robot interaction, in addition to the
human preferences, and physical abilities. For instance, the user
studies presented above are reflected by a configuration of costs
that favours approach motions by the side (Fig 10).
Visibility Criterion: Human comfort is a key issue when dealing
with HRI scenarios, and some properties can be extracted from this
issue. In particular, humans generally feel more comfortable when
the robot is within their field of vision. Therefore, a “visibility
criterion”, is used to help the robot to stay, during its motions, in
the human’s field of view. The visibility grid is constructed
according to costs reflecting the effort required by the human to get
the robot in his field of view. Grid points located in a direction for
which the human has only to move his eyes have a lower cost than
positions requiring head turning in order to get the robot in the field
of view. Also, when the robot is far away from the human, the
effect of visibility must decrease, and beyond a certain distance it
must be negligible.
Hidden Zones: In the grids presented above, the costs are
calculated without taking into account obstacles in the
environment. However, obstacles in close vicinity to the human can
have various effects on safety and visibility issues. If the robot is
behind an obstacle, the human might feel comfortable because the
obstacle would block the direct path between the human and the
robot. Therefore, the safety criterion must be cancelled in zones
located behind the obstacles. In contrast, as the robot passes behind
an obstacle and becomes hidden, and the human cannot see the
robot, the visibility costs no longer correspond to physical realities.
To handle this issue, we introduce a further criterion termed,
“hidden zones criterion”. This criterion helps to determine better
costs for positions hidden from the human by obstacles. An
important effect of obstacles for human comfort is the “surprise
factor”. When the robot is hidden by an obstacle close to the
human, and suddenly appears in the human field of vision, it can
cause surprise and possibly fear. To avoid this effect, we must
discourage the robot to pass behind an obstacle too closely, and
must allow it to get into the human’s field of view when sufficiently
far from the human. This can be done by adding costs to the zones
hidden from the subject’s view by the obstacles. The costs in the
hidden zone grid are inversely proportional to the distance between
the human and the robot so that the robot chooses to keep a
distance from back sides of the obstacles that are close to humans.
Once the safety, visibility, and hidden zones grids have been
computed (Fig. 10), they are merged into one single grid where the
robot will search for a minimum cost path.
Figure 10. The “safety”, the “visibility” and the
hidden zones” grids. The height of a point corresponds to the
cost of that point. The grids were modified to correspond to
the results of the user studies.
Figure 11. A human friendly path calculated
automatically by the planner. Note the robot does not choose
the shortest path and prefers a path that avoids it “to burst”
near the human.
Different ways, depending on the task and on the balance between
criteria, can be used to aggregate the grid costs.
For example, for an urgent task, the importance of the visibility
grid is less than the safety grid so that the robot does not take
visibility largely into account. Once the final grid is computed, the
cells corresponding to the obstacles in the environment are labelled
as forbidden and an A* search is performed to find minimum-cost
path between two given positions of the robot. Since only crossing
the obstacles and humans are forbidden, with this algorithm we
guarantee to find a path if it exists.
Figure 12. A Hallway scenario. The planner
automatically plans a trajectory that allows the robot to pass
next to the human without causing any discomfort. Note that
as the robot does not immediately take a position behind the
human, it avoids causing any discomfort when it is invisible to
him.
The computed paths shown in Figures 11 and 12 are collision-free
and also take into account the human’s comfort and safety.
3. Conclusions
Results from the two HRI trials indicate that a large majority of
human subjects, when seated, preferred a robot to approach from
either the left or right side. The frontal approach was seen as
uncomfortable, impractical, in some cases even threatening or
confrontational, and should thus be avoided. This result is in line
with human-human situations where standing or sitting at an angle
of 45 degrees to each other can reduce feelings of aggression and
confrontation [14]. However, the side that an individual human
will actually prefer, left or right, depends to a large extent on the
preferences of the individual concerned. The results do show that
there is a bias towards the right hand side. This may be related to
the fact that most of the trial subjects were right handed (in
common with most of the population in general). Therefore, for a
robot which is bringing an object to a seated human whose
preferences are not known, it should always avoid a frontal
approach and if (physically) convenient and consistent with the
particular task then approach from the right. If the seated humans’
approach direction preferences are known, then the robot should
approach from the preferred direction whenever convenient2. It
should be noted here that a human subject will not be unduly
disturbed if their approach preference with regard to which side are
not followed.
There were some perceived gender differences with regard to
approach direction, with some females actually preferring a frontal
approach direction, whereas slightly more males than females
2 Deriving such ‘social rules’ for robots from empirical HRI studies
is part of an attempt to develop a robotic etiquette, cf. B.
Ogden, K. Dautenhahn (2000) Robotic Etiquette: Structured
Interaction in Humans and Robots, in Proc SIRS2000, 8th
Symposium on Intelligent Robotic Systems, The University of
Reading, England, 18-20 July 2000.
preferred a right side approach over other directions. From
psychological studies [14] it has been found that women tend to
stand slightly closer to one another, face each other more, and
touch each other more, compared to men interacting with other
men. That women tend to face each other more could possibly
account for the fact that women in our studies more frequently
preferred the robot to approach from the frontal direction compared
to men, although this issue needs further investigation.
In the follow-up trials, no subjects thought that the robot came too
close from either the right or left side directions, though a majority
thought the front approach distance was too close. In all cases the
robot approached to no closer than 50cm, which was the inbuilt
safety collision avoidance distance of the robot. It has been noted
that there are cultural differences in personal spatial zones [14]3.
However, although some subjects in the HRI trials may have
originated from other countries and cultures, all the subjects had
been resident in the UK and therefore could be presumed to adopt
human-human social distances similar to those of the average UK
population. Therefore, regional, cultural or ethnic origin
information was not asked (or controlled) for in the studies4.
Most subjects stated that the robot moved too slowly or about right
at 0.4m/s, while nobody rated that the robot moved too fast. This
suggests that (especially after a longer habituation period), most
subjects would prefer the robot to move at a faster speed. It would
therefore be reasonable to set the default robot speed at a relatively
slow 0.4m/s and then perhaps increase the approach speed over
time or in response to the user’s wishes or preferences.
The robot used in the trials only had a simple short reach gripper,
so the object was presented to the subject in a simple lifting tray. If
a longer manipulator or arm was fitted, the results obtained may
well be very different. It is desirable to perform further trials with
various robots fitted with various types of arms or manipulators to
see what effect they may have on user preferences. Also, long term
trials are needed to investigate the effect on people of longer
periods of exposure to robots. It would also be interesting to
perform human-human studies to complement the work presented
here. However, the primary focus of this paper is on robot to
human approach direction preferences.
The human-aware motion planner is in its first steps of
development and implementation. It requires further experiments to
customize and validate the planner for live HRI situations. We are
planning to implement this motion planner along with task
reasoning capabilities [3] into a real robot that must have sufficient
3 For example, many southern Europeans and Japanese have an
intimate distance (reserved for close friends and family) of only
20-30cm compared to 46-122cm of the Americans and northern
Europeans. Europeans might refer to Asians as ‘pushy’ and
‘familiar’ and Asians might refer to Europeans and Americans as
‘cold’ and ‘stand-offish’. There are also differences in rural vs.
urban spatial zones. People raised in more rural, less populated
areas need more personal space, than those raised in densely
populated cities.
4 A specific study which investigates in more detail human robot
approach distances using PeopleBotTM robots is given in Walters
et al. [20].
human perception capabilities such as determination and tracking
of various features like human-body posture, head orientation, hand
configuration and gaze direction. In the execution stage of the plan,
the robot must be highly reactive to changes in the environment.
Using path deformation approaches can ensure this reactivity.
Joint work as described in this paper will ultimately contribute to
the development of interaction-aware robots [5], i.e. robots that
are sensitive to the social context they are embedded in. This is a
vital requirement for all those robotics applications where human
contact and acceptability plays a vital part, as it is the case in
domestic, healthcare and other applications. The challenge to
develop robots that are not only ‘doing the right thing’, but ‘doing
the thing right’ [15] can only be tackled in a interdisciplinary
endeavour involving psychologist as well as roboticists and HRI
experts.
4. ACKNOWLEDGMENTS
The work described in this paper was conducted within the EU
Integrated Project COGNIRON ("The Cognitive Robot
Companion") and was funded by the European Commission
Division FP6-IST Future and Emerging Technologies under
Contract FP6-002020.
Many thanks go to Mike Blow for his help in creating the videos.
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