Utilization of Image/Force/Tactile Sensor Data for Object-Shape-Oriented Manipulation: Wiping Objects With Turning Back Motions and Occlusion
Utilization of Image/Force/Tactile Sensor Data for Object-Shape-Oriented Manipulation: Wiping Objects With Turning Back Motions and Occlusion
复制标题
利用图像/力/触觉传感器数据进行面向对象形状的操作：通过向后运动和遮挡擦拭对象
DOI：
10.1109/lra.2021.3136657
复制
发表时间：
2022
影响因子：
5.2
通讯作者：
Sugano Shigeki
中科院分区：
文献类型：
--
作者：
Saito Namiko;Shimizu Takumi;Ogata Tetsuya;Sugano Shigeki
对处理各种物体的家务机器人的需求越来越大。然而，在传统的研究中，由于需要处理多个表面，不可见区域和遮挡，很难实现面向对象形状的任务;此外，机器人必须感知形状并调整运动，即使它们不能直接看到。人类通常通过整合多种感官信息来解决问题;受人类这种感知机制的启发，在这项研究中，我们考虑了有效利用图像/力/触觉数据来构建多模态深度神经网络（DNN）模型，用于物体感知和运动生成的形状。作为一个例子，我们构建了一个机器人来擦拭模仿光影的物体的外部。擦拭运动包括机器人的手必须远离表面的时刻以及擦拭下一个表面所需的转向方向，即使表面的一些部分（诸如背面或被机器人的臂遮挡的部分）可能无法直接看到。如果DNN模型使用连续的视觉信息，它会受到遮挡图像的严重影响。因此，性能最好的DNN模型是使用初始时间步长的图像来近似感知形状和大小，然后通过整合触觉和力的感知和感觉来生成运动的模型。我们的结论是，有效的方法，面向对象的操作是最初利用图像勾勒出目标形状，此后，使用力和触觉来理解具体的功能，同时执行任务。
There has been an increasing demand for housework robots to handle various objects. It is, however, difficult to achieve object-shape-oriented tasks in conventional research owing to the requirement for dealing with multiple surfaces, invisible area, and occlusion; moreover, robots must perceive shapes and adjust movements even if they cannot be seen directly. Humans usually tackle questions by integrating several sensory information; inspired by this perception mechanism of humans, in this study, we considered the effective utilization of image/force/tactile data in constructing a multimodal deep neural networks (DNN) model for the shape of an object perception and motion generation. As an example, we constructed a robot to wipe around the outside of objects that are imitating light shades. The wiping motions include the moment when the hands of the robot must be away from the surface as well as the turning directions required to wipe the next surface, even though some parts of the surfaces, such as the backside or parts occluded by the arm of the robot, may not be seen directly. If DNN model uses continuous visual information, it is badly influenced by the occluded images. Hence, the best-performing DNN model is the one that uses an image of the initial time-step to approximately perceive the shape and size and then generate motions by integrating the perception and sense of tactile and force. We conclude that the effective approach to object-shape-oriented manipulation is to initially utilize image to outline the target shape and, thereafter, to use force and tactile to understand concrete features while performing tasks.