Abstract
We present an attention based visual analysis framework to compute grasp-relevant information which helps to guide grasp planning using a multi-fingered robotic hand. Our approach uses a computational visual attention model to locate regions of interest in a scene and employ a deep convolutional neural network to detect grasp type and grasp attention point for a sub-region of the object in a region of interest. We demonstrate the proposed framework with object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide grasp planning. The effectiveness of the proposed approach is evaluated in both simulation experiments and real-world experiments. Experimental results show that the proposed framework can not only speed up grasp planning with more stable configurations, but also handle unknown objects. Furthermore, our framework can handle cluttered scenarios. A new Grasp Type Dataset (GTD) which includes six commonly used grasp types and covers 12 household objects is also presented.
Highlights
Imagine a toddler is in front of a table top with several objects, very likely he or she would interact with those objects by trying to pick up the red mug either by the handle or the rim, or trying to grasp the green ball
This paper proposes an attention based visual analysis framework, which computes grasp-relevant information directly from visual data for multi-fingered robotic grasping
By using the visual framework, an regions of interest (ROIs) is firstly localized by a computational attention model
Summary
Imagine a toddler is in front of a table top with several objects, very likely he or she would interact with those objects by trying to pick up the red mug either by the handle or the rim, or trying to grasp the green ball. This paper proposes an attention based visual analysis framework which directly locates sub-regions of objects as regions of interest (ROIs), and generates grasp-relevant information from visual data inside the ROIs for grasp planning with a multi-fingered robotic hand. There are few previous approaches that try to integrate grasp type detection into robotic grasp planning (Harada et al, 2008; Vahrenkamp et al, 2018) In those works, only two kinds of grasp types, i.e., power and precision (Napier, 1956), are considered, which is not sufficient for exploring the potential of multi-fingered robot hands. This work uses the results of the visual analysis for grasp planning with multi-fingered robotic hands.
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