Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots [Online]
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The unceasing
development of autonomous robots in many different scenarios drives a
new revolution to improve our quality of life. Recent advances in
human-robot interaction
and machine learning extend robots to social scenarios, where these
systems pretend
to assist humans in diverse tasks. Thus, social robots are nowadays
becoming real in
many applications like education, healthcare, entertainment, or
assistance. Complex
environments demand that social robots present adaptive mechanisms to
different situations and successfully execute their tasks. Thus,
considering the previous
ideas, making autonomous and appropriate decisions is essential to
exhibit reasonable
behaviour and operate well in dynamic scenarios.

Decision-making systems provide artificial agents with the capacity of
decisions about how to behave depending on input information from the
In the last decades, human decision-making has served researchers as an
inspiration to
endow robots with similar deliberation. Especially in social robotics,
where people expect
to interact with machines with human-like capabilities, biologically
inspired decision-making
systems have demonstrated great potential and interest. Thereby, it is
that these systems will continue providing a solid biological background
and improve the
naturalness of the human-robot interaction, usability, and the
acceptance of social robots
in the following years.
This thesis presents a decision-making system for social robots acting
in healthcare,
entertainment, and assistance with autonomous behaviour.

The system’s
goal is to
provide robots with natural and fluid human-robot interaction during the
realisation of
their tasks. The decision-making system integrates into an already
existing software
architecture with different modules that manage human-robot interaction,
or expressiveness. Inside this architecture, the decision-making system
decides which
behaviour the robot has to execute after evaluating information received
from different
modules in the architecture. These modules provide structured data about
activities, perceptions, and artificial biological processes that evolve
with time that are the
basis for natural behaviour. The natural behaviour of the robot comes
from the evolution
of biological variables that emulate biological processes occurring in
humans. We also
propose a Motivational model, a module that emulates biological
processes in humans for
generating an artificial physiological and psychological state that
influences the robot’s

These processes emulate the natural biological rhythms
of the human organism to produce biologically inspired decisions that
improve the naturalness exhibited
by the robot during human-robot interactions. The robot’s decisions also
depend on what
the robot perceives from the environment, planned events listed in the
robot’s agenda, and
the unique features of the user interacting with the robot.
The robot’s decisions depend on many internal and external factors that
influence how
the robot behaves. Users are the most critical stimuli the robot
perceives since they are
the cornerstone of interaction. Social robots have to focus on assisting
people in their
daily tasks, considering that each person has different features and
preferences. Thus,
a robot devised for social interaction has to adapt its decisions to
people that aim at
interacting with it. The first step towards adapting to different users
is identifying the user
it interacts with. Then, it has to gather as much information as
possible and personalise
the interaction. The information about each user has to be actively
updated if necessary
since outdated information may lead the user to refuse the robot.
Considering these facts,
this work tackles the user adaptation in three different ways.

• The robot incorporates user profiling methods to continuously gather
from the user using direct and indirect feedback methods.
• The robot has a Preference Learning System that predicts and adjusts
the user’s
preferences to the robot’s activities during the interaction.
• An Action-based Learning System grounded on Reinforcement Learning is
introduced as the origin of motivated behaviour.

The functionalities mentioned above define the inputs received by the
system for adapting its behaviour. Our decision-making system has been
for being integrated into different robotic platforms due to its
flexibility and modularity.
Finally, we carried out several experiments to evaluate the
architecture’s functionalities
during real human-robot interaction scenarios. In these experiments, we

• How to endow social robots with adaptive affective mechanisms to
interaction limitations.
• Active user profiling using face recognition and human-robot
• A Preference Learning System we designed to predict and adapt the
preferences towards the robot’s entertainment activities for adapting
the interaction.
• A Behaviour-based Reinforcement Learning System that allows the robot
to learn
the effects of its actions to behave appropriately in each situation.
• The biologically inspired robot behaviour using emulated biological
processes and
how the robot creates social bonds with each user.
• The robot’s expressiveness in affect (emotion and mood) and autonomic
such as heart rate or blinking frequency.
Universidad Carlos III de Madrid