128 research outputs found
Rare neural correlations implement robotic conditioning with delayed rewards and disturbances
Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms
Sensorimotor representation learning for an "active self" in robots: A model survey
Safe human-robot interactions require robots to be able to learn how to
behave appropriately in \sout{humans' world} \rev{spaces populated by people}
and thus to cope with the challenges posed by our dynamic and unstructured
environment, rather than being provided a rigid set of rules for operations. In
humans, these capabilities are thought to be related to our ability to perceive
our body in space, sensing the location of our limbs during movement, being
aware of other objects and agents, and controlling our body parts to interact
with them intentionally. Toward the next generation of robots with bio-inspired
capacities, in this paper, we first review the developmental processes of
underlying mechanisms of these abilities: The sensory representations of body
schema, peripersonal space, and the active self in humans. Second, we provide a
survey of robotics models of these sensory representations and robotics models
of the self; and we compare these models with the human counterparts. Finally,
we analyse what is missing from these robotics models and propose a theoretical
computational framework, which aims to allow the emergence of the sense of self
in artificial agents by developing sensory representations through
self-exploration
Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey
Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory
This thesis develops a theoretical framework for the generation of artificial societies. In particular
it shows how sub-systems emerge when the agents are able to learn and have the ability
to communicate.
This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated
originally by the German sociologist Luhmann, with concepts of Shannon's information
theory applied to adaptive learning agents.
Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational
modelling paradigm involving the modelling of phenomena as dynamical systems of
interacting agents. The thesis in particular, investigates the functions and properties necessary
to reproduce the paradigm of society by using the mentioned ABM approach.
Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems
can then be composed by agents with a reduced behavioural complexity. For example in
society there are people who produce goods and other who distribute them.
Both the behaviour and communication is learned by the agent and not imposed. The simulated
task is to collect food, keep it and eat it until sated. Every agent communicates its energy state
to the neighbouring agents. This results in two subsystems whereas agents in the first collect
food and in the latter steal food from others. The ratio between the number of agents that
belongs to the first system and to the second system, depends on the number of food resources.
Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise
by reducing the amount of sensory information or, equivalently, reducing the complexity of the
perceived environment from the agent's perspective. Shannon's information theorem is used
to assess the performance of the simulated learning agents. A practical measure, based on the
concept of Shannon's information
ow, is developed and applied to adaptive controllers which
use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning.
The behavioural complexity is measured with a novel information measure, called Predictive
Performance, which is able to measure at a subjective level how good an agent is performing
a task. This is then used to quantify the social division of tasks in a social group of honest,
cooperative food foraging, communicating agents
Augmenting user capabilities through an adaptive assistive manipulator
MenciĂłn Internacional en el tĂtulo de doctorAssistive robot manipulators have the potential to increase the independence of disabled
persons in activities of daily living. The current designs are mainly limited to pure teleoperation
by the user, given the need for keeping the user in the control loop, and the complexity of
the tasks and environments in which they operate. This thesis aims to augment the user’s
capabilities for performing such tasks by adapting the robot, and its level of assistance, to the
user. Methodologies for modeling and benchmarking the complete human-robot system were
established, which helped drive the development of different approaches to adaptation. This
included a task-oriented optimization of the robot physical structure, approaches for low-level
adaptive shared control, and work on interactive learning of, and assistance on completing, simple
object manipulation tasks. Three experimental platforms were used: The ASIBOT manipulator
of Universidad Carlos III de Madrid (UC3M), the AMOR manipulator of Exact Dynamics, and
the iCub humanoid robot.Los manipuladores asistenciales tienen el potencial de incrementar la independencia de personas
discapacitadas en sus actividades de la vida diaria. Los diseños actuales se limitan principalmente
a una pura teleoperaciĂłn, pues dada la complejidad de las tareas y del entorno, se
necesita mantener al usuario en el lazo de control. Esta tesis pretende mejorar las capacidades del
usuario para realizar estas tareas, adaptando el robot y su nivel de asistencia a las necesidades del
usuario. Se han establecido metodologĂas para el modelado y evaluaciĂłn del comportamiento del
sistema formado por humano y robot, lo que ha permitido el desarrollo de diferentes aproximaciones
a la adaptaciĂłn. Esto incluye desde la optimizaciĂłn de la estructura del robot atendiendo
a las tareas, la evaluaciĂłn de diversas aproximaciones al control compartido adaptativo a bajo
nivel, al aprendizaje interactivo y el desarrollo de asistencias para completar tareas sencillas de
manipulaciĂłn. Se ha hecho uso de tres plataformas experimentales: el manipulador ASIBOT de
la Universidad Carlos III de Madrid (UC3M), el manipulador AMOR de Exact Dynamics y el
humanoide iCub.Programa Oficial de Doctorado en IngenierĂa ElĂ©ctrica, ElectrĂłnica y AutomáticaPresidente: Alberto SanfeliĂş.- Secretario: ConcepciĂłn Alicia Monje Micharet.- Vocal: Yiannis Demiri
Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework
The idea of creating a general purpose machine intelligence that captures
many of the features of human cognition goes back at least to the earliest days
of artificial intelligence and neural computation. In spite of more than a
half-century of research on this issue, there is currently no existing approach
to machine intelligence that comes close to providing a powerful, general-purpose
human-level intelligence. However, substantial progress made during recent years
in neural computation, high performance computing, neuroscience and cognitive
science suggests that a renewed effort to produce a general purpose and adaptive
machine intelligence is timely, likely to yield qualitatively more powerful
approaches to machine intelligence than those currently existing, and certain
to lead to substantial progress in cognitive science, AI and neural computation.
In this report, we outline a conceptual framework for the long-term development
of a large-scale machine intelligence that is based on the modular organization,
dynamics and plasticity of the human brain. Some basic design principles are
presented along with a review of some of the relevant existing knowledge about
the neurobiological basis of cognition. Three intermediate-scale prototypes for
parts of a larger system are successfully implemented, providing support for the
effectiveness of several of the principles in our framework. We conclude that a
human-competitive neuromorphic system for machine intelligence is a viable long-
term goal, but that for the short term, substantial integration with more
standard symbolic methods as well as substantial research will be needed to make
this goal achievable
Adaptive networks for robotics and the emergence of reward anticipatory circuits
Currently the central challenge facing evolutionary robotics is to determine
how best to extend the range and complexity of behaviour supported by evolved
neural systems. Implicit in the work described in this thesis is the idea that this
might best be achieved through devising neural circuits (tractable to evolutionary
exploration) that exhibit complementary functional characteristics. We concentrate
on two problem domains; locomotion and sequence learning. For locomotion
we compare the use of GasNets and other adaptive networks. For sequence learning
we introduce a novel connectionist model inspired by the role of dopamine
in the basal ganglia (commonly interpreted as a form of reinforcement learning).
This connectionist approach relies upon a new neuron model inspired by notions
of energy efficient signalling. Two reward adaptive circuit variants were investigated.
These were applied respectively to two learning problems; where action
sequences are required to take place in a strict order, and secondly, where action
sequences are robust to intermediate arbitrary states. We conclude the thesis
by proposing a formal model of functional integration, encompassing locomotion
and sequence learning, extending ideas proposed by W. Ross Ashby.
A general model of the adaptive replicator is presented, incoporating subsystems
that are tuned to continuous variation and discrete or conditional events.
Comparisons are made with Ross W. Ashby's model of ultrastability and his
ideas on adaptive behaviour. This model is intended to support our assertion
that, GasNets (and similar networks) and reward adaptive circuits of the type
presented here, are intrinsically complementary. In conclusion we present some
ideas on how the co-evolution of GasNet and reward adaptive circuits might lead
us to significant improvements in the synthesis of agents capable of exhibiting
complex adaptive behaviour
A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks
Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs
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