Conference PaperPDF Available

How may i serve you? A robot companion approaching a seated person in a helping context

Authors:

Abstract and Figures

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.
Content may be subject to copyright.
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.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,
requires prior specific permission and/or a fee.
HRI’06. March 2-4, 2006, Salt Lake City, Utah, USA
Copyright 2006 ACM 1-59593-294-1/06/0003….$5.00
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.
5. REFERENCES
[1] Althaus, P., Ishiguro, H., Kanda, T., Miyashita, T. and
Christensen, H.I., Navigation for Human-Robot
Interaction Tasks. in Proc. IEEE Int. Conf. on Robotics &
Automation, (New Orleans, USA, 2004).
[2] Bicci, A. and Tonietti, G. Fast and soft arm tactics:
Dealing with the safety-performance trade-off in robot
arms design and control. IEEE Robotics and Automation
Magazine, 11 (2), 12-21.
[3] Clodic, A., Montreuil, V., Alami, R. and Chatila, R., A
decisional framework for autonomous robots. in Proc.
14th IEEE Int. Workshop on Robot & Human
Communication (RO-MAN), (Nashville, USA, 2005),
543-548.
[4] Dario, P., Guglieimelli, E. and Laschi, C. Humanoids and
personal robots: Design and experiments. Journal of
Robotic Systems, 18 (12), 673-690.
[5] Dautenhahn, K., Ogden, B. and Quick, T. From embodied
to socially embedded agents - Implications for interaction-
aware robots. Cognitive Systems Research, 3 (3), 397-
428.
[6] Dautenhahn, K., Woods, S., Kaouri, C., Walters, M.,
Koay, K.L. and Werry, I., What is a robot companion -
Friend, assistant or butler? in Proc. IEEE IROS,
(Edmonton, Canada, 2005), 1488-1493.
[7] Fong, T., Nourbakhsh, I. and Dautenhahn, K. A survey of
socially interactive robots. Robotics and Autonomous
Systems, 42, 143-166.
[8] Hinds, P., Roberts, T. and Jones, L. Whose job is it
anyway? A study of human-robot interaction in a
collaborative task. Human Computer Interaction, 19, 151-
181.
[9] Kanda, T., Hirano, T., Eaton, D. and Ishiguro, H.
Interactive robots as social partners and peer tutors for
children: A field trial. Human Computer Interaction, 19
(1-2), 61-24.
[10] Kulic, D. and Croft, E., Safe Planning for Human-Robot
Interaction. in Proc. IEEE Int. Conf on Robotics &
Automation, (New Orleans, USA, 2004).
[11] Kulic, D. and Croft, E., Strategies for safety in human-
robot interaction. in Proc. IEEE Int. Conf. on Advanced
Robotics, (2003), 810-815.
[12] Latombe, J.C. Robot motion planning. Kluwer Academic
Publishers, Boston, USA, 1991.
[13] Pacchierotti, E., Christensen, H.I. and Jensfelt, P., Human-
robot embodied interaction in hallway settings: A pilot
user study. in Proc. 14th IEEE Int. Workshop on Robot &
Human Interactive Communication (RO-MAN),
(Nashville, USA, 2005), 164-171.
[14] Pease A. and Pease B. The definitive book of body
language. London. Orion Books Ltd, 2004.
[15] Sengers, P., Do the right thing: An architecture for action
expression. in Proc. Second Int. Conf. on Autonomous
Agents, (1998), 24-31.
[16] Severinson-Eklundh, K., Green, A. and Hüttenrauch, H.
Social and collaborative aspects of interaction with a
service robot. Robotics and Autonomous Systems, 42.
223-234.
[17] Te Boekhorst, R., Walters, M.L., Koay, K.L.,
Dautenhahn, K. and Nehaniv, C. A study of a single robot
interacting with groups of children in a rotation game
scenario. In Proc. of IEEE CIRA 2005, (Finland, 2005).
[18] Walters, M., Woods, S., Koay, K.L. and Dautenhahn, K.,
Practical and methodological challenges in designing and
conducting interaction studies with human subjects. In
Proc. of AISB'05, (University of Hertfordshire, Hatfield,
UK, 2005), 110-119.
[19] Walters, M.L, Dautenhahn, K., Te Boekhorst, R., Koay,
K.L, Kaouri, C, Woods, S, Nehaniv, C, Lee D. and Werry,
I. The influence of subjects' personality traits on personal
spatial zones in a human-robot interaction experiment. .in
Proc. 14th IEEE Int. Workshop on Robot & Human
Communication (RO-MAN), (Nashville, USA, 2005),
347-352.
[20] Walters M. L, Dautenhahn K, Koay K. L, Kaouri C, te
Boekhorst R, Nehaniv C. L , Werry I and Lee D. Close
encounters: Spatial distances between people and a robot
of mechanistic appearance. Proc. IEEE-RAS Humanoids
2005, December 5-7, 2005, Tsukuba, Japan., 450-455
[21] Zim, M., Khatib, B., Roth, B. and Salisbury, J.K. Playing
it safe (human friendly robots). IEEE Robotics and
Automation Magazine, 11 (2), 12-21.
... As an example, the European Parliament report [31] considered psychological consequences of HRI: "you (denoting users) are permitted to make use of a robot without risk or fear of physical or psychological harm" (p. 23). Within this context, this article aims to address perceived safety in detail. ...
... Perceived safety and human comfort are often mentioned together in HRI literature [10,23,52,54], sometimes used even as synonyms. Therefore, comfort is a criterion that is closely related to perceived safety. ...
... Functional properties Physical contact [72,92] Pre-warning [21] Emergency buttons [69,93] Robot competence [69,72] Visual interface [12,36,87] Audible interface [69,87] Performance [43,88] Task and role [44,78] Level of autonomy [20,37,56] Failures [37,42] False alarms [28,37] Action and Movement Speed and velocity [78,92] Approach speed, direction, distance [23,51,78,93] Proximity [37], [78] Motion trajectory, fluency [78,88] Predictable motion [69,78] Physical Properties Physical appearance [37,56,66,78] Anthropomorphism [37,46,78] Social Properties Communication [18,78] Eye gaze, contact [69,84] Robot personality [37,44] Sociability [88] Interaction duration, frequency [59] robot must be sufficiently safe, comfortable, natural, transparent, and predictable for people in the same environment. These qualities can be achieved with a holistic approach. ...
Article
Full-text available
Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.
... A tight integration of Robotics and AI is crucial to enhance the autonomy and control capabilities of robots and allow them to safely and reliably act in the real-world [41,48]. In particular, robots acting in the real world should take into account a number of "non-functional" qualities that are crucial to realize behaviors that are safe and acceptable with respect to humans [15,28,75]. Robot controllers should therefore evolve towards an advanced "Perception, Reason, Act" paradigm implementing the cognitive capabilities needed to synthesize and execute flexible behaviors that are valid from both a technical and social point of view. ...
... If more than one method is found (i.e., |I M,i | > 1) then each method m i,j ∈ I M,i describes an alternative procedure associated with ProductionGoal g i (rows [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. If only one method is found (|I M,i | == 1) then a single decomposition is associated with ProductionGoal g i (rows [21][22][23][24][25][26][27][28][29][30]. ...
... It then extracts all the triples matching the pattern ?m i,j dul:hasConstituent ?t∠ to find the set of individuals I T ,j of ProdcutionTask decomposing ProductionGoal g i (row 25). For each task t k ∈ I T ,j , the procedure creates a task node n k and associates it with the AND node n AND i through the edge n AND i , n k (rows [26][27][28][29]. At ...
Article
Full-text available
The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.
... Within HCI, etiquette plays a critical role in the day-to-day design of AI agents. Commonly, this etiquette manifests itself through a variety of behavioral designs, such as the inclusion of turn-taking behaviors (Miller, 2011), the reinforcement of an AI's role (Dautenhahn et al., 2006), and even the utilization of polite language during conversation (Schiaffino & Amandi, 2006). When incorporated, these behaviors have demonstrable impacts on human-agent interactions. ...
... The first AI teammate was designed to adhere to general etiquette standards, including deliberate turn taking by asking humans if they need help (Miller, 2011), not leaving its role unless told to by the humans (Dautenhahn et al., 2006), and using polite language (Schiaffino & Amandi, 2006). The AI teammate could still perform its responsibility autonomously but would require a directive to search for trip components other than the flight. ...
... Rec 3: Input from AI Teammates Should Improve Their Human Teammates' Work not Override it. While the design of etiquette ignorance within this study considers has AI teammates act outside their assigned role (Dautenhahn et al., 2006), that does not mean that AI systems should be designed to override human influence. Multiple participants in this study were not accepting of their etiquette, ignoring AI teammates since they felt it had diminished their role as a teammate. ...
Article
Full-text available
Technical and practical advancements in Artificial Intelligence (AI) have led to AI teammates working alongside humans in an area known as human-agent teaming. While critical past research has shown the benefit to trust driven by the incorporation of interaction rules and structures (i.e. etiquette) in both AI tools and robotic teammates, research has yet to explicitly examine etiquette for digital AI teammates. Given the historic importance of trust within human-agent teams, the identification of etiquette’s impact within said teams should be paramount. Thus, this study empirically evaluates the impact of AI teammate etiquette through a mixed-methods study that compares AI teammates that either adhere to or ignore traditional etiquette standards for machine systems. The quantitative results show that traditional etiquette adherence leads to greater trust, perceived performance of the AI, and perceived performance of the team as a whole. However, qualitative results reveal that not all traditional etiquette behaviors have universal appeal due to the presence of individual differences. This research provides the first empirical and explicit exploration of etiquette within human-agent teams, and the results of this study should be used further design specific etiquette behaviors for AI teammates.
... Fetch-and-carry tasks are a common application of mobile robots, typically contextualized as an assistive robot in household [7,35] or warehouse environments [3]. In this context, both social navigation in human-populated spaces [8,34] and the direct social interactions with humans in the space, e.g., conversations [31] and handover/delivery actions [16,26], are important fields of research. Trajectory planning in the presence of humans considers aspects such as safety and visibility to the human, while also abiding by social conventions or human preferences. ...
... 6 Participants did not receive any reimbursement for their participation. Figures 6,7,8,9,and 10 show the results from the survey questions for each outcome variable. Two-way repeated measures ANOVA tests were first conducted between gaze behaviors and navigation scenarios to confirm whether an interaction effect exists between the two variables. ...
Article
Full-text available
As robots have become increasingly common in human-rich environments, it is critical that they are able to exhibit social cues to be perceived as a cooperative and socially-conformant team member. We investigate the effect of robot gaze cues on people’s subjective perceptions of a mobile robot as a socially present entity in three common hallway navigation scenarios. The tested robot gaze behaviors were path-oriented (looking at its own future path), or human-oriented (looking at the nearest person), with fixed-gaze as the control. We conduct a real-world study with 36 participants who walked through the hallway, and an online study with 233 participants who were shown simulated videos of the same scenarios. Our results suggest that the preferred gaze behavior is scenario-dependent. Human-oriented gaze behaviors which acknowledge the presence of the human are generally preferred when the robot and human cross paths. However, this benefit is diminished in scenarios that involve less implicit interaction between the robot and the human.
... Psychological and social cues may impact the human user's perception of the robot [4,18]. These aspects can be essential to understand how to design robot behaviors that are appropriate in different contexts. ...
Article
Full-text available
A robot intended to monitor human behavior must account for the user’s reactions to minimize his/her perceived discomfort. The possibility of learning user interaction preferences and changing the robot’s behavior accordingly may positively impact the perceived quality of the interaction with the robot. The robot should approach the user without causing any discomfort or interference. In this work, we contribute and implement a novel Reinforcement Learning (RL) approach for robot navigation toward a human user. Our implementation is a proof-of-concept that uses data gathered from real-world experiments to show that our algorithm works on the kind of data that it would run on in a realistic scenario. To the best of our knowledge, our work is one of the first attempts to provide an adaptive navigation algorithm that uses RL to account for non-deterministic phenomena.
... One way to enable robots to establish a natural interaction is by developing human norms in them [12]. Among different proposed robot behavior models, those that adjust their behavior based on humans' social norms are more preferred [13]. ...
Article
Full-text available
Recent studies in the field of Human–Robot Interaction (HRI) confirm the positive effects of robots’ empathic behaviors in HRI. Most HRI studies investigating empathy, apply an empirical approach to implement empathy, i.e., the empathic model is derived directly from observations of empathic actions. This resulted in the emergence of numerous different empathic models that are only valid for a particular scenario that is highly tuned and, therefore, a slight modification in the scenario makes the corresponding empathy model infeasible. In fact, most of the proposed models suffer from a lack of generalizability. Since empathy is a complex concept that includes different dimensions, a coherent model of empathy that can be used in different scenarios or even be scenario independent, needs to consider several core concepts of empathy.Thus, the goal of this paper is to analyze and link different concepts of empathy and bring related existing models together, which can help researchers in the HRI community to have a better picture of an empathy model that might lead to the development of more general models of empathy for social robots.
... For these tasks, how to navigate in human-populated spaces is an important field of research. Trajectory planning in the presence of humans [9,10] considers aspects such as safety and visibility to the human, while also abiding by social conventions or human preferences. Proxemics [11,12] considers the distance a robot should maintain from a human, which is important to maximizing the robot's perceived safety. ...
Preprint
As robots have become increasingly common in human-rich environments, it is critical that they are able to exhibit social cues to be perceived as a cooperative and socially-conformant team member. We investigate the effect of robot gaze cues on people's subjective perceptions of a mobile robot as a socially present entity in three common hallway navigation scenarios. The tested robot gaze behaviors were path-oriented (looking at its own future path), or person-oriented (looking at the nearest person), with fixed-gaze as the control. We conduct a real-world study with 36 participants who walked through the hallway, and an online study with 233 participants who were shown simulated videos of the same scenarios. Our results suggest that the preferred gaze behavior is scenario-dependent. Person-oriented gaze behaviors which acknowledge the presence of the human are generally preferred when the robot and human cross paths. However, this benefit is diminished in scenarios that involve less implicit interaction between the robot and the human.
... We defined this position guided by the findings of [13], as it facilitates robot visibility for people waiting, important for legibility of intent, and people usually feel more comfortable when the robot is within their field of view. [7]. This position is also more efficient for the robot to enter the elevator, providing the shortest possible path from a position that does not block the elevator doors. ...
Preprint
Full-text available
Interactions between humans and autonomous mobile robots (AMRs) are expected to grow in smart cities to improve logistics operations, such as depositing packages on AMRs for pickup on the street. However, the way that humans walk and pass objects to an AMR when approaching each other remains largely unknown. We conducted two psychophysical experiments to clarify the behavior and comfort of humans when carrying a package and placing it on an AMR for load carrying. Participants were asked to approach a programmed AMR and pass the package in two experiments: 1) changing the stop distance and AMR speed and 2) changing the stop distance and package weight. Motion trackers quantified the participants' walking speed and frequency of hesitation to walk. In addition, the subjective heaviness and comfort were recorded through a questionnaire during each trial. The results indicated that the participants' speed decreased and hesitation probability increased when the stop distance of the AMR decreased. Nevertheless, the participants felt more comfortable with the close approach, whereas the package weight did not affect their behavior. By contrast, they felt uncomfortable when AMR remained still. These findings suggest that humans regard the AMR approach as load-carrying assistance and not as invading their personal space. To achieve a comfortable interaction in load carrying from humans to AMRs, we suggest that the AMR can closely approach a person without eliciting personal space invasion.
Article
Full-text available
Takayuki Kanda is a computer scientist with interests in intelligent robots and human-robot interaction; he is a researcher in the Intelligent Robotics and Communication Laboratories at ATR (Advanced Telecommunications Re-search Institute), Kyoto, Japan. Takayuki Hirano is a computer scientist with an interest in human–robot interaction; he is an intern researcher in the Intelli-gent Robotics and Communication Laboratories at ATR, Kyoto, Japan. Daniel Eaton is a computer scientist with an interest in human–robot interaction; he is an intern researcher in the Intelligent Robotics and Communication Labora-tories at ATR, Kyoto, Japan. Hiroshi Ishiguro is a computer scientist with in-terests in computer vision and intelligent robots; he is Professor of Adaptive Machine Systems in the School of Engineering at Osaka University, Osaka, Ja-pan, and a visiting group leader in the Intelligent Robotics and Communication Laboratories at ATR, Kyoto, Japan. ABSTRACT Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speak-ing "Robovie" robots interacted with first-and sixth-grade pupils at the perime-ter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition.
Conference Paper
Full-text available
The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.
Chapter
In Chapter 1 we introduced configuration space as a space in which the robot maps to a point. The mathematical structure of this space, however, is not completely straightforward, and deserves some specific consideration. The purpose of this chapter and the next one is to provide the reader with a general understanding of this structure when the robot is a rigid object not constrained by any kinematic or dynamic constraint. This chapter mainly focuses on topological and differential properties of the configuration space. More detailed algebraic and geometric properties related to the mapping of the obstacles into configuration space will be investigated in Chapter 3.
Book
1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.
Article
This paper presents a hierarchical path planning and control strategy for ensuring safety during a human-robot interaction. At the planning stage, a two-step process is used where first the danger of the interaction is minimized, followed by a goal seeking optimization. This approach reduces the likelihood of encountering local minima due to conflicts between reducing danger and a demanded interaction task. At the control stage, the human intent signal is evaluated at every step to ensure safe operation of the robot. In initial simulation work, the controller drives the robot away from the planned interaction if danger is identified, and then allows the planned interaction to resume once the danger has passed.
Article
This paper reviews “socially interactive robots”: robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of “social robots”. We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002].
Article
This paper addresses the field of humanoid and personal robotics—its objectives, motivations, and technical problems. The approach described in the paper is based on the analysis of humanoid and personal robots as an evolution from industrial to advanced and service robotics driven by the need for helpful machines, as well as a synthesis of the dream of replicating humans. The first part of the paper describes the development of anthropomorphic components for humanoid robots, with particular regard to anthropomorphic sensors for vision and touch, an eight-d.o.f. arm, a three-fingered hand with sensorized fingertips, and control schemes for grasping. Then, the authors propose a user-oriented design methodology for personal robots, and describe their experience in the design, development, and validation of a real personal robot composed of a mobile unit integrating some of the anthropomorphic components introduced previously and aimed at operating in a distributed working environment. Based on the analysis of experimental results, the authors conclude that humanoid robotics is a tremendous and attractive technical and scientific challenge for robotics research. The real utility of humanoids has still to be demonstrated, but personal assistance can be envisaged as a promising application domain. Personal robotics also poses difficult technical problems, especially related to the need for achieving adequate safety, proper human–robot interaction, useful performance, and affordable cost. When these problems are solved, personal robots will have an excellent chance for significant application opportunities, especially if integrated into future home automation systems, and if supported by the availability of humanoid robots.