3,491 research outputs found
Evolutionary robotics : anticipation and the reality gap
Evolutionary Robotics provide efficient tools and approach to address automatic design of controllers for automous mobile robots. However, the computational cost of the optimization process makes it difficult to evolve controllers directly into the real world. This paper addresses the key problem of tranferring into the real world a robotic controller that has been evolved in a robotic simulator. The approach presented here relies on the definition of an anticipation-enabled control architecture. The anticipation module is able to build a partial model of the simulated environment and, once in the real world, performs an error estimation of this model. This error can be reused so as to perform in-situ on-line adaptation of robot control. Experiments in simulation and real-world showed that an evolved robot is able to perform on-line recovery from several kind of locomotion perturbations
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
Modeling Life as Cognitive Info-Computation
This article presents a naturalist approach to cognition understood as a
network of info-computational, autopoietic processes in living systems. It
provides a conceptual framework for the unified view of cognition as evolved
from the simplest to the most complex organisms, based on new empirical and
theoretical results. It addresses three fundamental questions: what cognition
is, how cognition works and what cognition does at different levels of
complexity of living organisms. By explicating the info-computational character
of cognition, its evolution, agent-dependency and generative mechanisms we can
better understand its life-sustaining and life-propagating role. The
info-computational approach contributes to rethinking cognition as a process of
natural computation in living beings that can be applied for cognitive
computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201
Exploring New Horizons in Evolutionary Design of Robots
International audienceThis introduction paper to the 2009 IROS workshop “Exploring new horizons in Evolutionary Design of Robots” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems and (c) automatic synthesis, which corresponds to the automatic design of a mechatronic device. Critical issues will also be presented as well as current trends and pespectives in ER
On the Fundamentality of Meaning
The mainstream view of meaning is that it is emergent, not fundamental, but some have disputed this, asserting that there is a more fundamental level of reality than that addressed by current physical theories, and that matter and meaning are in some way entangled. In this regard there are intriguing parallels between the quantum and biological domains, suggesting that there may be a more fundamental level underlying both. I argue that the organisation of this fundamental level is already to a considerable extent understood by biosemioticians, who have fruitfully integrated Peirce’s sign theory into biology; things will happen there resembling what happens with familiar life, but the agencies involved will differ in ways reflecting their fundamentality, in other words they will be less complex, but still have structures complex enough for what they have to do. According to one approach involving a collaboration with which I have been involved, a part of what they have to do, along with the need to survive and reproduce, is to stop situations becoming too chaotic, a concept that accords with familiar ‘edge of chaos’ ideas.
Such an extension of sign theory (semiophysics?) needs to be explored by physicists, possible tools being computational models, existing insights into complexity, and dynamical systems theory. Such a theory will not be mathematical in the same way that conventional physics theories are mathematical: rather than being foundational, mathematics will be ‘something that life does’, something that sufficiently evolved life does because in the appropriate context so doing is of value to life
Robustness in the long run: Auto-teaching vs Anticipation in Evolutionary Robotics
In Evolutionary Robotics, auto-teaching networks, neural networks that modify their own weights during the life-time of the robot, have been shown to be powerful architectures to develop adaptive controllers. Unfortunately, when run for a longer period of time than that used during evolution, the long-term behavior of such networks can become unpredictable. This paper gives an example of such dangerous behavior, and proposes an alternative solution based on anticipation: as in auto-teaching networks, a secondary network is evolved, but its outputs try to predict the next state of the robot sensors. The weights of the action network are adjusted using some back-propagation procedure based on the errors made by the anticipatory network. First results -- in simulated environments -- show a tremendous increase in robustness of the long-term behavior of the controller
Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines
The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature
A cooperative active perception approach for swarm robotics
More than half a century after modern robotics first emerged, we still face
a landscape in which most of the work done by robots is predetermined, rather
than autonomous. A strong understanding of the environment is one of the key
factors for autonomy, enabling the robots to make correct decisions based on the
environment surrounding them.
Classic methods for obtaining robotic controllers are based on manual specification, but become less trivial as the complexity scales. Artificial intelligence
methods like evolutionary algorithms were introduced to synthesize robotic controllers by optimizing an artificial neural network to a given fitness function that
measures the robots’ performance to solve a predetermined task.
In this work, a novel approach to swarm robotics environment perception is
studied, with a behavior model based on the cooperative identification of objects
that fly around an environment, followed by an action based on the result of the
identification process. Controllers are obtained via evolutionary methods. Results
show a controller with a high identification and correct decision rates.
The work is followed by a study on scaling up that approach to multiple environments. Experiments are done on terrain, marine and aerial environments,
as well as on ideal, noisy and hybrid scenarios. In the hybrid scenario, different
evolution samples are done in different environments. Results show the way these
controllers are able to adapt to each scenario and conclude a hybrid evolution is
the best fit to generate a more robust and environment independent controller to
solve our task.Mais de um século após a robótica moderna ter surgido, ainda nos deparamos
com um cenário onde a maioria do trabalho executado por robôs é pré-determinado,
ao invés de autónomo. Uma forte compreensão do ambiente é um dos pontos chave
para a autonomia, permitindo aos robôs tomarem decisões corretas baseadas no
ambiente que os rodeia.
Abordagens mais clássicas para obter controladores de robótica são baseadas na
especificação manual, mas tornam-se menos apropriadas à medida que a complexidade aumenta. Métodos de inteligência artificial como algoritmos evolucionários
foram introduzidos para obter controladores de robótica através da otimização de
uma rede neuronal artificial para uma função de fitness que mede a aptidão dos
robôs para resolver uma determinada tarefa.
Neste trabalho, é apresentada uma nova abordagem para perceção do ambiente
por um enxame de robôs, com um modelo de comportamento baseado na identificação cooperativa de objetos que circulam no ambiente, seguida de uma atuação
baseada no resultado da identificação. Os controladores são obtidos através de
métodos evolucionários. Os resultados apesentam um controlador com uma alta
taxa de identificação e de decisão.
Segue-se um estudo sobre o escalonamento da abordagem a múltiplos ambientes. São feitas experiencias num ambiente terrestre, marinho e aéreo, bem
como num contexto ideal, ruidoso e híbrido. No contexto híbrido, diferentes samples da evolução ocorrem em diferentes ambientes. Os resultados demonstram a
forma como cada controlador se adapta aos restantes ambientes e concluem que a
evolução híbrida foi a mais capaz de gerar um controlador robusto e transversal
aos diferentes ambientes.
Palavras-chave: Robótica evolucionária, Sistemas multi-robô, Cooperação,
Perceção, Identificação de objetos, Inteligência artificial, Aprendizagem automática,
Redes neuronais, Múltiplos ambientes
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
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