Motivations and emotional control

interfaz2

Description

RESEARCH TOPIC DESCRIPTION

This research is part of a large project which its main objective is to build an autonomous and social robot. The robot must learn to select the right behaviours in order to achieve its goals. The mechanisms involved in the decision making process are inspired on those used by humans and animals. Since it is a social robot, one of the required features would be the life-like appearance. The social aspect
of the robot will be reflected in the fact that the human
interaction is not going to be considered as a complement of the
rest of functionalities of the robot, but as one of its basic
features. For this kind of robots autonomy and emotions make
them behave as if they were ”alive”. This feature would help
people to think of them not as simple machines, but as real
companions. For certain applications, a robot with its own
personality is more attractive than another that simply executes the
actions that it is programmed to do.

Many decision making architectures based on emotions have been
implemented previously on robots. Most of them place emphasis on the external expression of
the emotions (Breazeal,2002), (Fujita, 2001), (Shibata 1999).
These robots have the possibility of expressing emotions through
facial expressions and, sometimes, through their body gestures. In
this case, emotions can be considered as a particular type of
information, which is exchanged in the human-robot interaction.
In nature, emotions have different purposes, and interaction is only
one of them. But it has been proved that emotions have a fundamental role in human behaviour and social interaction. They also influence cognitive processes,
particularly problem solving and decision making (Damasio, 1994).
Emotions can also act as control and learning mechanisms (Fong, 2002). In this project, emotions are used for trying to
imitate their natural function in learning processes and decision making.

Before the implementation of this system on a real robot, as a
previous step, a decison making system has been developed using virtual
agents, instead of robots. The agent lives in a virtual world where
objects, needed to survive, and other agents exist. This agent must
learn a policy of behaviour to survive, maintaining all his needs
inside acceptable ranges. The policies establish a normative about
what to do in each situation. This means that the agent must learn
the right relation between states and actions. In this system the agent
knows the properties of every object, i.e. the agent knows which
actions can be executed with each object. What the agent does not
know is which action is right in each situation. In order to carry
out this learning process, the agent uses reinforcement learning
algorithms.

In this reseach it is considered that the role each emotion plays, and how its associated mechanisms
work are very specific. This implies that each emotion must be
implemented on the robot/agent in a particular way. In this system the following emotions have been implemented:happiness, sadness and fear.

The emotions of happiness and sadness have been defined as the
positive and negative variation of the wellbeing of the agent/robot. The
wellbeing measures the degree of satisfaction of the drives or needs of the agent. It has been proved that, in order to learn a right
policy of behaviour, the reinforcement function must be happiness
and sadness. Therefore, these emotions are used as positive and negative
rewards. Fear is presented from two points of view: to be afraid of executing
risky actions, or to be afraid of being in a dangerous state. In
this last case, fear is considered as a motivation, in accordance
with other emotions theories.

We have proved that emotions are useful in the decision making system since happiness
and sadness are used as positive and negative rewards. Therefore,
these emotions are essential for the learning of policies. In
relation to fear, when the agent uses fear to avoid risky actions,
he improves his quality of life. Moreover, the use of Fear as a
motivation makes the agent to learn to escape from dangerous states.
Therefore, it has been proved the usefulness of the emotion fear.

Entries:
A model-free approach for accurate joint motion control in humanoid locomotion
International Journal of Humanoid Robotics. num. 1 , vol. 8 , 2011
J. Villagra
Humanoid Robot RH-1 for Collaborative Tasks. A Control Architecture for Human-Robot Cooperation
Applied Bionics and Biomechanics. num. 4 , vol. 5 , pages: 225 – 234 , 2009
C.A. Monje P. Pierro

Entries:
O. Stasse; A. Kheddar; K. Yokoi. Humanoid feet trajectory generation for the reduction of the dynamical effects
The 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids '09), Paris, France
P. Pierro
A Human-Humanoid Interface for Collaborative Tasks
Second workshop for young researchers on Human-friendly robotics, Sestri Levante, Italy
P. Pierro M. González-Fierro D. Hernandez
A Practical Decoupled Stabilizer for Joint-Position Controlled Humanoid Robots
The 2009 IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS '09), St. Louis, USA
D. Kaynov P. Pierro
The Virtual COM Joints Approach for Whole-Body RH-1 Motion
18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN '09), Toyama , Japan
P. Pierro C.A. Monje
Performing collaborative tasks with the humanoid robot RH-1 – A novel control architecture
12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR '09), Istanbul, Turkey
P. Pierro C.A. Monje
Pose Control of the Humanoid Robot RH-1 for Mobile Manipulation
14th International Conference on Advanced Robotics (ICAR '09), Munich, Germany
P. Pierro C.A. Monje
Capítulo: “Realización de tareas colaborativas entre robots humanoides. Experimentación con dos robots Robonova”
At Proceedings of the V Workshop ROBOCITY2030. Cooperación en Robótica, 2009, Madrid, Spain
D. Herrero P. Pierro A. Jardon
Modelling and Control of the Humanoid Robot RH-1 for Collaborative Tasks
IEEE RAS/RSJ Conference on Humanoids Robots, Daejeon, Korea
P. Pierro C.A. Monje
Robots in future collaborative working environments
First workshop for young researchers on Human-friendly robotics, Napoli, Italy
P. Pierro
HUMAN-HUMANOID ROBOT COOPERATION IN COLLABORATIVE TRANSPORTATION TASKS
11th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2008), 2008, Coimbra, Portugal
M. Arbulu
Trends of new robotics platform, designing Humanoid Robot Rh-1
CARS & FOF 0723rd ISPE International Conference on CAD/CAM Robotics and Factories of the Future, 2007, Bogota, Colombia
M. Arbulu D. Kaynov L.M. Cabas P. Staroverov
Nuevas tendencias en plataformas de robótica, caso robot humanoide Rh-1
Intercon 2007XIV Congreso Internacional de Ingeniería Eléctrica, Electrónica y Sistemas, 2007, Piura, Peru
M. Arbulu D. Kaynov L.M. Cabas P. Staroverov
ZMP Human Measure System
8th International Conference on Climbing and Walking Robots (Clawar'2005), London, United Kingdom
M. Arbulu D. Kaynov P. Staroverov
Rh-0 Humanoid Robot Bipedal Locomotion and Navigation Using Lie Groups and Geometric Algorithms
International Conference on Intelligent Robots and Systems (IROS'2005), Edmonton, Canada
J. M. Pardos-Gotor
Humanoid Robot Kinematics Modeling Using Lie Groups
7th International Conference on Climbing and Walking Robots (Clawar'2004), Madrid, Spain
J. M. Pardos-Gotor
Lie Groups and Lie Algebras in Robotics.
University Carlos III of Madrid – ROBOTICSLAB SEMINAR., Madrid, Spain
J. M. Pardos-Gotor

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