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Figure 2: The Care-O-Bot, a personal care robot.
2 RELATED WORK
The field of robotics research that focuses on human-
robot interaction and collaboration is one of the more
recent and interdisciplinary areas.
Rios-Martinez et al. (2015) discuss a domain
called Social Signal Processing. They estimate that
over 60% of communication between two people
come from nonverbal communication, i.e. body lan-
guage and social cues. It discusses the connection be-
tween social cues (e.g. hand gestures and posture)
and social signals (e.g. emotion, personality, status)
and the importance of this connection in future re-
search of human-robot interactions. The survey also
looks at proxemics and various spaces such as com-
mon models of the personal space, which are mostly
static and non-adaptable. Interaction spaces, related
to groups of people, affordance or activity and spaces
related to objects are discussed and it is apparent that
modelling these spaces efficiently is important for so-
cially acceptable robot navigation. The possibility of
a dynamic personal space model is briefly mentioned,
and in our paper the concept of an adaptable personal
space is explored.
Dautenhahn et al. (2006) carried out human-robot
interaction trials to determine the preferred approach
direction and other defining characteristics of a robot
approach. Results based on a live trial showed that
the most preferred direction was to the right or left
hand side of the person and the least preferred direc-
tion was from the front. The approaches were also
rated in terms of practicality (seen in relation to the
trial environment) and comfort, and the frontal ap-
proach was again the least preferred or lowest rated.
In our work we found the same trend of people prefer-
ring non-frontal or direct approaches when it comes to
judging the closeness of the robot. The paper also dis-
cusses the idea of combining safety, visibility and hid-
den zone criteria that together seek to model the cost
map of an environment. The cost is modelled based
on e.g. whether the person can get the robot into his
field of view by moving just his eyes or if he needs to
turn his head. The cost function also tries to penalize
the robot for making surprising appearances, such as
when coming from a hidden zone that the person can-
not see and into the persons field of view. Instead, the
robot should seek to enter the persons field of view at
a comfortable distance so there is enough time for the
person to react and be aware of the robot. In our work
we use a cost function which is based on the position
of a persons body and hands. We also vary the ori-
entation of the robot so the robot either looks in the
direction of travel or tries to maintain a sort of eye-
contact by looking at the person.
Kirby et al. (2009) implement a navigation frame-
work where human social conventions such as per-
sonal space and tending to one side of hallways are
represented as constraints on a robot’s navigation.
The following constraints were identified as impor-
tant for social behaviour in hallway situations; Min-
imize travelled distance, obstacle avoidance, person
avoidance with personal space and passing on the
right hand side, default velocity where the robot tries
to keep a constant velocity and inertia where the robot
should try to keep moving straight as much as pos-
sible. Each of these constraints are weighted and
combined linearly and used as the objective function
in a modified A
∗
planner. The method was success-
ful in generating paths that resemble human-like be-
haviour when moving down a hallway; passing on-
coming traffic on the right and cutting across the hall-
way based on how far oncoming traffic is. In our work
we utilize a sampling based method for path planning.
Our cost function is also based on a personal space
model, but our model is adaptable instead of constant.
Woods et al. (2006) investigate differences, be-
tween live and video based trials, in responses and
preferences for different characteristics of robot ap-
proaches. Participants, both in the live and video
based trials, were questioned about their preferred
approach direction, stopping distance and approach
speed. There were a high levels of correspondence
(85% resp. 87%) for the least preferred approach di-
rection (frontal) and the ratings of the robot’s speed.
Moderate to high (60-80%) agreement was found be-
tween video and live trials for most preferred ap-
proach direction and the robot’s stopping distance
from the subject. Almost all subjects (93%) preferred
the live trials over the videos. These results are inter-
esting, since in our paper we utilize videos of simu-
lated approaches and online questionnaires for evalu-
ation. This evaluation method is a lot faster, allows
for more experimentation and does not have the same
Socially Acceptable Behaviour for Robots Approaching Humans using an Adaptable Personal Space
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