142 research outputs found
Straight-Leg Walking Through Underconstrained Whole-Body Control
We present an approach for achieving a natural, efficient gait on bipedal
robots using straightened legs and toe-off. Our algorithm avoids complex height
planning by allowing a whole-body controller to determine the straightest
possible leg configuration at run-time. The controller solutions are biased
towards a straight leg configuration by projecting leg joint angle objectives
into the null-space of the other quadratic program motion objectives. To allow
the legs to remain straight throughout the gait, toe-off was utilized to
increase the kinematic reachability of the legs. The toe-off motion is achieved
through underconstraining the foot position, allowing it to emerge naturally.
We applied this approach of under-specifying the motion objectives to the Atlas
humanoid, allowing it to walk over a variety of terrain. We present both
experimental and simulation results and discuss performance limitations and
potential improvements.Comment: Submitted to 2018 IEEE International Conference on Robotics and
Automatio
A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots
This paper contributes towards the development and comparison of
Divergent-Component-of-Motion (DCM) based control architectures for humanoid
robot locomotion. More precisely, we present and compare several DCM based
implementations of a three layer control architecture. From top to bottom,
these three layers are here called: trajectory optimization, simplified model
control, and whole-body QP control. All layers use the DCM concept to generate
references for the layer below. For the simplified model control layer, we
present and compare both instantaneous and Receding Horizon Control
controllers. For the whole-body QP control layer, we present and compare
controllers for position and velocity control robots. Experimental results are
carried out on the one-meter tall iCub humanoid robot. We show which
implementation of the above control architecture allows the robot to achieve a
walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201
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
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