Recalling of Multiple Grasping Methods From an Object Image With a Convolutional Neural Network
In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The robot learns grasping methods using a convolutional neural network to observe the grasping activities of human without special instructions. For this setting, only one grasping motion is observed for an object at a time. By automatically clustering the observed grasping postures, the robot learns multiple grasping methods. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions. The network for recalling the grasping position estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position. The method then checks the graspability for each estimated position. Finally, it recalls the hand shapes based on the estimated grasping position and the object’s shape. This study shows the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method.
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Posted 29 Dec, 2020
Received 25 Feb, 2021
On 14 Feb, 2021
On 14 Feb, 2021
Invitations sent on 13 Feb, 2021
On 20 Dec, 2020
On 20 Dec, 2020
On 20 Dec, 2020
On 18 Dec, 2020
Recalling of Multiple Grasping Methods From an Object Image With a Convolutional Neural Network
Posted 29 Dec, 2020
Received 25 Feb, 2021
On 14 Feb, 2021
On 14 Feb, 2021
Invitations sent on 13 Feb, 2021
On 20 Dec, 2020
On 20 Dec, 2020
On 20 Dec, 2020
On 18 Dec, 2020
In this study, a method for a robot to recall multiple grasping methods for a given object is proposed. The robot learns grasping methods using a convolutional neural network to observe the grasping activities of human without special instructions. For this setting, only one grasping motion is observed for an object at a time. By automatically clustering the observed grasping postures, the robot learns multiple grasping methods. In the proposed method, the grasping methods are clustered during the process of learning of the grasping position. The method first recalls grasping positions. The network for recalling the grasping position estimates the multi-channel heatmap such that each channel heatmap indicates one grasping position. The method then checks the graspability for each estimated position. Finally, it recalls the hand shapes based on the estimated grasping position and the object’s shape. This study shows the results of recalling multiple grasping methods and demonstrates the effectiveness of the proposed method.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Due to technical limitations, table 1 is only available as a download in the Supplemental Files section.