379 research outputs found
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
Central pattern generators (CPGs), with a basis is neurophysiological
studies, are a type of neural network for the generation of rhythmic motion.
While CPGs are being increasingly used in robot control, most applications are
hand-tuned for a specific task and it is acknowledged in the field that generic
methods and design principles for creating individual networks for a given task
are lacking. This study presents an approach where the connectivity and
oscillatory parameters of a CPG network are determined by an evolutionary
algorithm with fitness evaluations in a realistic simulation with accurate
physics. We apply this technique to a five-link planar walking mechanism to
demonstrate its feasibility and performance. In addition, to see whether
results from simulation can be acceptably transferred to real robot hardware,
the best evolved CPG network is also tested on a real mechanism. Our results
also confirm that the biologically inspired CPG model is well suited for legged
locomotion, since a diverse manifestation of networks have been observed to
succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization,
and quantitative result
Biped dynamic walking using reinforcement learning
This thesis presents a study of biped dynamic walking using reinforcement learning. A hardware biped robot was built. It uses low gear ratio DC motors in order to provide free leg movements. The Self Scaling Reinforcement learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. A new learning architecture was designed to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of modules that represent new knowledge, or new requirements for the desired task. Control experiments were carried out using a simulator and the physical biped. The biped learned dynamic walking on flat surfaces without any previous knowledge about its dynamic model
Generation and control of locomotion patterns for biped robots by using central pattern generators
This paper presents an efficient closed-loop locomotion control system for biped robots that operates in the joint space. The robot’s joints are directly driven through control signals generated by a central pattern generator (CPG) network. A genetic algorithm is applied in order to find out an optimal combination of internal parameters of the CPG given a desired walking speed in straight line. Feedback signals generated by the robot’s inertial and force sensors are directly fed into the CPG in order to automatically adjust the locomotion pattern over uneven terrain and to deal with external perturbations in real time. Omnidirectional motion is achieved by controlling the pelvis motion. The performance of the proposed control system has been assessed through simulation experiments on a NAO humanoid robot
Multisensor Input for CPG-Based Sensory—Motor Coordination
International audienceThis paper describes a method for providing in real time a reliable synchronization signal for cyclical motions such as steady-state walking. The approach consists in estimating online a phase variable on the basis of several implicit central pattern generator associated with a set of sensors. These sensors can be of any kind, provided their output strongly reflects the timedmotion of a link. They can be, for example, spatial position or orientation sensors, or foot sole pressure sensors. The principle of the method is to use their outputs as inputs to nonlinear observers of modified Van der Pol oscillators that provide us with several independent estimations of the overall phase of the system. These estimations are then combined within a dynamical filter constituted of a Hopf oscillator. The resulting phase is a reliable indexing of the cyclic behavior of the system, which can finally be used as input to low-level controllers of a robot. Some results illustrate the efficiency of the approach, which can be used to control robots
Locomoção de humanoides robusta e versátil baseada em controlo analĂtico e fĂsica residual
Humanoid robots are made to resemble humans but their locomotion
abilities are far from ours in terms of agility and versatility. When humans
walk on complex terrains or face external disturbances, they
combine a set of strategies, unconsciously and efficiently, to regain
stability. This thesis tackles the problem of developing a robust omnidirectional
walking framework, which is able to generate versatile
and agile locomotion on complex terrains. We designed and developed
model-based and model-free walk engines and formulated the
controllers using different approaches including classical and optimal
control schemes and validated their performance through simulations
and experiments. These frameworks have hierarchical structures that
are composed of several layers. These layers are composed of several
modules that are connected together to fade the complexity and
increase the flexibility of the proposed frameworks. Additionally, they
can be easily and quickly deployed on different platforms.
Besides, we believe that using machine learning on top of analytical approaches
is a key to open doors for humanoid robots to step out of laboratories.
We proposed a tight coupling between analytical control and
deep reinforcement learning. We augmented our analytical controller
with reinforcement learning modules to learn how to regulate the walk
engine parameters (planners and controllers) adaptively and generate
residuals to adjust the robot’s target joint positions (residual physics).
The effectiveness of the proposed frameworks was demonstrated and
evaluated across a set of challenging simulation scenarios. The robot
was able to generalize what it learned in one scenario, by displaying
human-like locomotion skills in unforeseen circumstances, even in the
presence of noise and external pushes.Os robĂ´s humanoides sĂŁo feitos para se parecerem com humanos,
mas suas habilidades de locomoção estão longe das nossas em termos
de agilidade e versatilidade. Quando os humanos caminham em
terrenos complexos ou enfrentam distĂşrbios externos combinam diferentes
estratégias, de forma inconsciente e eficiente, para recuperar a
estabilidade. Esta tese aborda o problema de desenvolver um sistema
robusto para andar de forma omnidirecional, capaz de gerar uma locomoção
para robôs humanoides versátil e ágil em terrenos complexos.
Projetámos e desenvolvemos motores de locomoção sem modelos e
baseados em modelos. Formulámos os controladores usando diferentes
abordagens, incluindo esquemas de controlo clássicos e ideais,
e validámos o seu desempenho por meio de simulações e experiências
reais. Estes frameworks têm estruturas hierárquicas compostas por
várias camadas. Essas camadas são compostas por vários módulos
que sĂŁo conectados entre si para diminuir a complexidade e aumentar
a flexibilidade dos frameworks propostos. Adicionalmente, o sistema
pode ser implementado em diferentes plataformas de forma fácil.
Acreditamos que o uso de aprendizagem automática sobre abordagens
analĂticas Ă© a chave para abrir as portas para robĂ´s humanoides
saĂrem dos laboratĂłrios. Propusemos um forte acoplamento entre controlo
analĂtico e aprendizagem profunda por reforço. Expandimos o
nosso controlador analĂtico com mĂłdulos de aprendizagem por reforço
para aprender como regular os parâmetros do motor de caminhada
(planeadores e controladores) de forma adaptativa e gerar resĂduos
para ajustar as posições das juntas alvo do robĂ´ (fĂsica residual). A
eficácia das estruturas propostas foi demonstrada e avaliada em um
conjunto de cenários de simulação desafiadores. O robô foi capaz de
generalizar o que aprendeu em um cenário, exibindo habilidades de
locomoção humanas em circunstâncias imprevistas, mesmo na presença
de ruĂdo e impulsos externos.Programa Doutoral em Informátic
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
The complexity of bipedal locomotion may be attributed to the difficulty in
synchronizing joint movements while at the same time achieving high-level
objectives such as walking in a particular direction. Artificial central
pattern generators (CPGs) can produce synchronized joint movements and have
been used in the past for bipedal locomotion. However, most existing CPG-based
approaches do not address the problem of high-level control explicitly. We
propose a novel hierarchical control mechanism for bipedal locomotion where an
optimized CPG network is used for joint control and a neural network acts as a
high-level controller for modulating the CPG network. By separating motion
generation from motion modulation, the high-level controller does not need to
control individual joints directly but instead can develop to achieve a higher
goal using a low-dimensional control signal. The feasibility of the
hierarchical controller is demonstrated through simulation experiments using
the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the
controller's ability to function even without the availability of an exact
robot model.Comment: In: Proceedings of the Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo,
Norway, Aug. 19-22, 201
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