1,812 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
Design, modeling and implementation of a soft robotic neck for humanoid robots
Mención Internacional en el título de doctorSoft humanoid robotics is an emerging field that combines the flexibility and safety of soft
robotics with the form and functionality of humanoid robotics. This thesis explores the potential
for collaboration between these two fields with a focus on the development of soft joints for the
humanoid robot TEO. The aim is to improve the robot’s adaptability and movement, which are
essential for an efficient interaction with its environment.
The research described in this thesis involves the development of a simple and easily transportable
soft robotic neck for the robot, based on a 2 Degree of Freedom (DOF) Cable Driven
Parallel Mechanism (CDPM). For its final integration into TEO, the proposed design is later
refined, resulting in an efficiently scaled prototype able to face significant payloads.
The nonlinear behaviour of the joints, due mainly to the elastic nature of their soft links,
makes their modeling a challenging issue, which is addressed in this thesis from two perspectives:
first, the direct and inverse kinematic models of the soft joints are analytically studied,
based on CDPM mathematical models; second, a data-driven system identification is performed
based on machine learning techniques. Both approaches are deeply studied and compared, both
in simulation and experimentally.
In addition to the soft neck, this thesis also addresses the design and prototyping of a soft
arm capable of handling external loads. The proposed design is also tendon-driven and has a
morphology with two main bending configurations, which provides more versatility compared
to the soft neck.
In summary, this work contributes to the growing field of soft humanoid robotics through
the development of soft joints and their application to the humanoid robot TEO, showcasing the
potential of soft robotics to improve the adaptability, flexibility, and safety of humanoid robots.
The development of these soft joints is a significant achievement and the research presented in this thesis paves the way for further exploration and development in this field.La robótica humanoide blanda es un campo emergente que combina la flexibilidad y seguridad
de la robótica blanda con la forma y funcionalidad de la robótica humanoide. Esta
tesis explora el potencial de colaboración entre estos dos campos centrándose en el desarrollo
de una articulación blanda para el cuello del robot humanoide TEO. El objetivo es mejorar la
adaptabilidad y el movimiento del robot, esenciales para una interacción eficaz con su entorno.
La investigación descrita en esta tesis consiste en el desarrollo de un prototipo sencillo
y fácilmente transportable de cuello blando para el robot, basado en un mecanismo paralelo
actuado por cable de 2 grados de libertad. Para su integración final en TEO, el diseño propuesto
es posteriormente refinado, resultando en un prototipo eficientemente escalado capaz de manejar
cargas significativas.
El comportamiemto no lineal de estas articulaciones, debido fundamentalmente a la naturaleza
elástica de sus eslabones blandos, hacen de su modelado un gran reto, que en esta tesis
se aborda desde dos perspectivas diferentes: primero, los modelos cinemáticos directo e inverso
de las articulaciones blandas se estudian analíticamente, basándose en modelos matemáticos de
mecanismos paralelos actuados por cable; segundo, se aborda el problema de la identificación
del sistema mediante técnicas basadas en machine learning. Ambas propuestas se estudian y
comparan en profundidad, tanto en simulación como experimentalmente.
Además del cuello blando, esta tesis también aborda el diseño de un brazo robótico blando
capaz de manejar cargas externas. El diseño propuesto está igualmente basado en accionamiento
por tendones y tiene una morfología con dos configuraciones principales de flexión, lo que
proporciona una mayor versatilidad en comparación con el cuello robótico blando.
En resumen, este trabajo contribuye al creciente campo de la robótica humanoide blanda
mediante el desarrollo de articulaciones blandas y su aplicación al robot humanoide TEO, mostrando el potencial de la robótica blanda para mejorar la adaptabilidad, flexibilidad y seguridad
de los robots humanoides. El desarrollo de estas articulaciones es una contribución
significativa y la investigación presentada en esta tesis allana el camino hacia nuevos desarrollos
y retos en este campo.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Cecilia Elisabet García Cena.- Secretario: Dorin Sabin Copaci.- Vocal: Martin Fodstad Stole
From virtual demonstration to real-world manipulation using LSTM and MDN
Robots assisting the disabled or elderly must perform complex manipulation
tasks and must adapt to the home environment and preferences of their user.
Learning from demonstration is a promising choice, that would allow the
non-technical user to teach the robot different tasks. However, collecting
demonstrations in the home environment of a disabled user is time consuming,
disruptive to the comfort of the user, and presents safety challenges. It would
be desirable to perform the demonstrations in a virtual environment. In this
paper we describe a solution to the challenging problem of behavior transfer
from virtual demonstration to a physical robot. The virtual demonstrations are
used to train a deep neural network based controller, which is using a Long
Short Term Memory (LSTM) recurrent neural network to generate trajectories. The
training process uses a Mixture Density Network (MDN) to calculate an error
signal suitable for the multimodal nature of demonstrations. The controller
learned in the virtual environment is transferred to a physical robot (a
Rethink Robotics Baxter). An off-the-shelf vision component is used to
substitute for geometric knowledge available in the simulation and an inverse
kinematics module is used to allow the Baxter to enact the trajectory. Our
experimental studies validate the three contributions of the paper: (1) the
controller learned from virtual demonstrations can be used to successfully
perform the manipulation tasks on a physical robot, (2) the LSTM+MDN
architectural choice outperforms other choices, such as the use of feedforward
networks and mean-squared error based training signals and (3) allowing
imperfect demonstrations in the training set also allows the controller to
learn how to correct its manipulation mistakes
Modification of Gesture-Determined-Dynamic Function with Consideration of Margins for Motion Planning of Humanoid Robots
The gesture-determined-dynamic function (GDDF) offers an effective way to
handle the control problems of humanoid robots. Specifically, GDDF is utilized
to constrain the movements of dual arms of humanoid robots and steer specific
gestures to conduct demanding tasks under certain conditions. However, there is
still a deficiency in this scheme. Through experiments, we found that the
joints of the dual arms, which can be regarded as the redundant manipulators,
could exceed their limits slightly at the joint angle level. The performance
straightly depends on the parameters designed beforehand for the GDDF, which
causes a lack of adaptability to the practical applications of this method. In
this paper, a modified scheme of GDDF with consideration of margins (MGDDF) is
proposed. This MGDDF scheme is based on quadratic programming (QP) framework,
which is widely applied to solving the redundancy resolution problems of robot
arms. Moreover, three margins are introduced in the proposed MGDDF scheme to
avoid joint limits. With consideration of these margins, the joints of
manipulators of the humanoid robots will not exceed their limits, and the
potential damages which might be caused by exceeding limits will be completely
avoided. Computer simulations conducted on MATLAB further verify the
feasibility and superiority of the proposed MGDDF scheme
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