136 research outputs found
Cortical Models for Movement Control
Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)
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
Digital control networks for virtual creatures
Robot control systems evolved with genetic algorithms traditionally take the form
of floating-point neural network models. This thesis proposes that digital control systems,
such as quantised neural networks and logical networks, may also be used for
the task of robot control. The inspiration for this is the observation that the dynamics
of discrete networks may contain cyclic attractors which generate rhythmic behaviour,
and that rhythmic behaviour underlies the central pattern generators which drive lowlevel
motor activity in the biological world.
To investigate this a series of experiments were carried out in a simulated physically
realistic 3D world. The performance of evolved controllers was evaluated on two well
known control tasks—pole balancing, and locomotion of evolved morphologies. The
performance of evolved digital controllers was compared to evolved floating-point neural
networks. The results show that the digital implementations are competitive with
floating-point designs on both of the benchmark problems. In addition, the first reported
evolution from scratch of a biped walker is presented, demonstrating that when
all parameters are left open to evolutionary optimisation complex behaviour can result
from simple components
Locomoção bípede adaptativa a partir de uma única demonstração usando primitivas de movimento
Doutoramento em Engenharia EletrotécnicaEste trabalho aborda o problema de capacidade de imitação da locomoção
humana através da utilização de trajetórias de baixo nível codificadas com
primitivas de movimento e utilizá-las para depois generalizar para novas
situações, partindo apenas de uma demonstração única. Assim, nesta linha de
pensamento, os principais objetivos deste trabalho são dois: o primeiro é
analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas
por um sistema de captura de movimento de forma a modelar tarefas de
locomoção bípede. Contudo, esta transferência não está limitada à simples
reprodução desses movimentos, requerendo uma evolução das capacidades
para adaptação a novas situações, assim como lidar com perturbações
inesperadas. Assim, o segundo objetivo é o desenvolvimento e avaliação de
uma estrutura de controlo com capacidade de modelação das ações, de tal
forma que a demonstração única apreendida possa ser modificada para o robô
se adaptar a diversas situações, tendo em conta a sua dinâmica e o ambiente
onde está inserido.
A ideia por detrás desta abordagem é resolver o problema da generalização a
partir de uma demonstração única, combinando para isso duas estruturas
básicas. A primeira consiste num sistema gerador de padrões baseado em
primitivas de movimento utilizando sistemas dinâmicos (DS). Esta abordagem
de codificação de movimentos possui propriedades desejáveis que a torna ideal
para geração de trajetórias, tais como a possibilidade de modificar determinados
parâmetros em tempo real, tais como a amplitude ou a frequência do ciclo do
movimento e robustez a pequenas perturbações. A segunda estrutura, que está
embebida na anterior, é composta por um conjunto de osciladores acoplados
em fase que organizam as ações de unidades funcionais de forma coordenada.
Mudanças em determinadas condições, como o instante de contacto ou
impactos com o solo, levam a modelos com múltiplas fases. Assim, em vez de
forçar o movimento do robô a situações pré-determinadas de forma temporal, o
gerador de padrões de movimento proposto explora a transição entre diferentes
fases que surgem da interação do robô com o ambiente, despoletadas por
eventos sensoriais. A abordagem proposta é testada numa estrutura de
simulação dinâmica, sendo que várias experiências são efetuadas para avaliar
os métodos e o desempenho dos mesmos.This work addresses the problem of learning to imitate human locomotion actions
through low-level trajectories encoded with motion primitives and generalizing
them to new situations from a single demonstration. In this line of thought, the
main objectives of this work are twofold: The first is to analyze, extract and
encode human demonstrations taken from motion capture data in order to model
biped locomotion tasks. However, transferring motion skills from humans to
robots is not limited to the simple reproduction, but requires the evaluation of
their ability to adapt to new situations, as well as to deal with unexpected
disturbances. Therefore, the second objective is to develop and evaluate a
control framework for action shaping such that the single-demonstration can be
modulated to varying situations, taking into account the dynamics of the robot
and its environment.
The idea behind the approach is to address the problem of generalization from
a single-demonstration by combining two basic structures. The first structure is
a pattern generator system consisting of movement primitives learned and
modelled by dynamical systems (DS). This encoding approach possesses
desirable properties that make them well-suited for trajectory generation, namely
the possibility to change parameters online such as the amplitude and the
frequency of the limit cycle and the intrinsic robustness against small
perturbations. The second structure, which is embedded in the previous one,
consists of coupled phase oscillators that organize actions into functional
coordinated units. The changing contact conditions plus the associated impacts
with the ground lead to models with multiple phases. Instead of forcing the robot’s
motion into a predefined fixed timing, the proposed pattern generator explores
transition between phases that emerge from the interaction of the robot system
with the environment, triggered by sensor-driven events. The proposed approach
is tested in a dynamics simulation framework and several experiments are
conducted to validate the methods and to assess the performance of a humanoid
robot
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
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