1,318 research outputs found
A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.
In the present work we demonstrate that the current Child-Computer Interaction
paradigm is not potentiating human development to its fullest – it is associated with
several physical and mental health problems and appears not to be maximizing children’s
cognitive performance and cognitive development. In order to potentiate children’s
physical and mental health (including cognitive performance and cognitive development)
we have developed a new approach to human development and evolution.
This approach proposes a particular synergy between the developing human body,
computing machines and natural environments. It emphasizes that children should be
encouraged to interact with challenging physical environments offering multiple possibilities
for sensory stimulation and increasing physical and mental stress to the organism.
We created and tested a new set of computing devices in order to operationalize
our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In
two initial studies we were able to observe that the main goal of our approach is being
achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural
environment, managed to trigger a different neurophysiological response (increases
in sustained attention levels) and tended to optimize episodic memory performance in
children, compared to interaction with a sedentary screen-based computing device, in
an artificially controlled environment (indoors) - thus a promising solution to promote
cognitive performance/development; and that interaction with the Biosymtic device
“Cratus”, in a natural environment, instilled vigorous physical activity levels in children
- thus a promising solution to promote physical and mental health
A biologically inspired meta-control navigation system for the Psikharpax rat robot
A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics
Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer überregionalen Konsortiallizenz frei zugänglich.This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired but also provides new avenues for modulating the balance between plasticity and stability of the models.H2020 Marie Skłodowska-Curie Actionshttps://doi.org/10.13039/100010665Horizon 2020 Framework Programmehttps://doi.org/10.13039/100010661Peer Reviewe
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems
In this article, we propose a backpropagation-free approach to robotic
control through the neuro-cognitive computational framework of neural
generative coding (NGC), designing an agent built completely from powerful
predictive coding/processing circuits that facilitate dynamic, online learning
from sparse rewards, embodying the principles of planning-as-inference.
Concretely, we craft an adaptive agent system, which we call active predictive
coding (ActPC), that balances an internally-generated epistemic signal (meant
to encourage intelligent exploration) with an internally-generated instrumental
signal (meant to encourage goal-seeking behavior) to ultimately learn how to
control various simulated robotic systems as well as a complex robotic arm
using a realistic robotics simulator, i.e., the Surreal Robotics Suite, for the
block lifting task and can pick-and-place problems. Notably, our experimental
results demonstrate that our proposed ActPC agent performs well in the face of
sparse (extrinsic) reward signals and is competitive with or outperforms
several powerful backprop-based RL approaches.Comment: Contains appendix with pseudocode and additional detail
Autonomous decision-making for socially interactive robots
Mención Internacional en el título de doctorThe aim of this thesis is to present a novel decision-making system
based on bio-inspired concepts to decide the actions to make during
the interaction between humans and robots. We use concepts from
nature to make the robot may behave analogously to a living being
for a better acceptance by people. The system is applied to
autonomous Socially Interactive Robots that works in environments
with users. These objectives are motivated by the need of having
robots collaborating, entertaining or helping in educational tasks for
real situations with children or elder people where the robot has to
behave socially. Moreover, the decision-making system can be
integrated into this kind of robots in order to learn how to act
depending on the user profile the robot is interacting with. The
decision-making system proposed in this thesis is a solution to all
these issues in addition to a complement for interactive learning in
HRI. We also show real applications of the system proposed applying
it in an educational scenario, a situation where the robot can learn
and interact with different kinds of people. The last goal of this
thesis is to develop a robotic architecture that is able to learn how to
behave in different contexts where humans and robots coexist. For
that purpose, we design a modular and portable robotic architecture
that is included in several robots. Including well-known software
engineering techniques together with innovative agile software
development procedures that produces an easily extensible
architecture.El objetivo de esta tesis es presentar un novedoso sistema de toma de
decisiones basado en conceptos bioinspirados para decidir las acciones
a realizar durante la interacción entre personas y robots. Usamos
conceptos de la naturaleza para hacer que el robot pueda comportarse
análogamente a un ser vivo para una mejor aceptación por las personas.
El sistema está desarrollado para que se pueda aplicar a los llamados
Robots Socialmente Interactivos que están destinados a entornos con
usuarios. Estos objetivos están motivados por la necesidad de tener
robots en tareas de colaboración, entretenimiento o en educación en
situaciones reales con niños o personas mayores en las cuales el robot
debe comportarse siguiendo las normas sociales. Además, el sistema
de toma de decisiones es integrado en estos tipos de robots con el fin
de que pueda aprender a actuar dependiendo del perfil de usuario con
el que el robot está interactuando. El sistema de toma de decisiones
que proponemos en esta tesis es una solución a todos estos desafíos
además de un complemento para el aprendizaje interactivo en la
interacción humano-robot. También mostramos aplicaciones reales del
sistema propuesto aplicándolo en un escenario educativo, una situación
en la que el robot puede aprender e interaccionar con diferentes
tipos de personas. El último objetivo de esta tesis es desarrollar un
arquitectura robótica que sea capaz de aprender a comportarse en
diferentes contextos donde las personas y los robots coexistan. Con ese
propósito, diseñamos una arquitectura robótica modular y portable
que está incluida en varios robots. Incluyendo técnicas bien conocidas
de ingeniería del software junto con procedimientos innovadores de
desarrollo de sofware ágil que producen una arquitectura fácilmente
extensible.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Martin Stoele
Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system
It is true that the "best" neural network is not necessarily the one with the
most "brain-like" behavior. Understanding biological intelligence, however, is
a fundamental goal for several distinct disciplines. Translating our
understanding of intelligence to machines is a fundamental problem in robotics.
Propelled by new advancements in Neuroscience, we developed a spiking neural
network (SNN) that draws from mounting experimental evidence that a number of
individual neurons is associated with spatial navigation. By following the
brain's structure, our model assumes no initial all-to-all connectivity, which
could inhibit its translation to a neuromorphic hardware, and learns an
uncharted territory by mapping its identified components into a limited number
of neural representations, through spike-timing dependent plasticity (STDP). In
our ongoing effort to employ a bioinspired SNN-controlled robot to real-world
spatial mapping applications, we demonstrate here how an SNN may robustly
control an autonomous robot in mapping and exploring an unknown environment,
while compensating for its own intrinsic hardware imperfections, such as
partial or total loss of visual input.Comment: 8 pages, 3 Figures, International Conference on Neuromorphic Systems
(ICONS 2018
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