9 research outputs found
Throwing Objects into A Moving Basket While Avoiding Obstacles
The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target basket, located outside its feasible kinematic space, without traveling to the desired location. In previous approaches, the robot often learned a parameterized throwing kernel through analytical approaches, imitation learning, or hand-coding. There are many situations in which such approaches do not work/generalize well due to various object shapes, heterogeneous mass distribution, and also obstacles that might be presented in the environment. It is obvious that a method is needed to modulate the throwing kernel through its meta parameters. In this paper, we tackle object throwing problem through a deep reinforcement learning approach that enables robots to precisely throw objects into moving baskets while there are obstacles obstructing the path. To the best of our knowledge, we are the first group that addresses throwing objects with obstacle avoidance. Such a throwing skill not only increases the physical reachability of a robot arm but also improves the execution time. In particular, the robot detects the pose of the target object, basket, and obstacle at each time step, predicts the proper grasp configuration for the target object, and then infers appropriate parameters to throw the object into the basket. Due to safety constraints, we develop a simulation environment in Gazebo to train the robot and then use the learned policy in real-robot directly. To assess the performers of the proposed approach, we perform extensive sets of experiments in both simulation and real robots in three scenarios. Experimental results showed that the robot could precisely throw a target object into the basket outside its kinematic range and generalize well to new locations and objects without colliding with obstacles
MVGrasp:Real-time multi-view 3D object grasping in highly cluttered environments
Nowadays robots play an increasingly important role in our daily life. In human-centered environments, robots often encounter piles of objects, packed items, or isolated objects. Therefore, a robot must be able to grasp and manipulate different objects in various situations to help humans with daily tasks. In this paper, we propose a multi-view deep learning approach to handle robust object grasping in human-centric domains. In particular, our approach takes a point cloud of an arbitrary object as an input, and then, generates orthographic views of the given object. The obtained views are finally used to estimate pixel-wise grasp synthesis for each object. We train the model end-to-end using a synthetic object grasp dataset and test it on both simulation and real-world data without any further fine-tuning. To evaluate the performance of the proposed approach, we performed extensive sets of experiments in four everyday scenarios, including isolated objects, packed items, pile of objects, and highly cluttered scenes. Experimental results show that our approach performed very well in all simulation and real-robot scenarios. More specifically, the proposed approach outperforms previous state-of-the-art approaches and achieves a success rate of in all simulated and real scenarios, except for the pile of objects which is 82%. Additionally, our method demonstrated reliable closed-loop grasping of novel objects in a variety of scene configurations. The video of our experiments can be found here: https://youtu.be/c-4lzjbF7fY
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
Simultaneous multi-view object recognition and grasping in open-ended domains
To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, object recognition and grasping are two key functionalities for autonomous robots. Most state-of-the-art approaches treat object recognition and grasping as two separate problems, even though both use visual input. Furthermore, the knowledge of the robot is fixed after the training phase. In such cases, if the robot encounters new object categories, it must be retrained to incorporate new information without catastrophic forgetting. In order to resolve this problem, we propose a deep learning architecture with an augmented memory capacity to handle open-ended object recognition and grasping simultaneously. In particular, our approach takes multi-views of an object as input and jointly estimates pixel-wise grasp configuration as well as a deep scale- and rotation-invariant representation as output. The obtained representation is then used for open-ended object recognition through a meta-active learning technique. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings. A video of these experiments is available online at: this https UR