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Research Article
Makoto SANADA
Ritsumeikan Daigaku Joho Rikogakubu Daigakuin Joho Rikogaku Kenkyuka
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5905-0318
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Keywords
object grasping, convolutional neural network, recalling grasping method, clustering
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at ROBOMECH Journal.
Version 1
Posted 29 Dec, 2020
No community comments so far
Editorial decision: Minor revision
On 27 Mar, 2021
Review #1 received
Received 25 Feb, 2021
Review #2 received
Received 25 Feb, 2021
Reviewer #2 agreed
On 14 Feb, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 13 Feb, 2021
Editor assigned
On 20 Dec, 2020
Submission checks complete
On 20 Dec, 2020
Editor invited
On 20 Dec, 2020
First submitted
On 18 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurology
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See the published version of this preprint at ROBOMECH Journal.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Makoto SANADA
Ritsumeikan Daigaku Joho Rikogakubu Daigakuin Joho Rikogaku Kenkyuka
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5905-0318
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at ROBOMECH Journal.
Version 1
Posted 29 Dec, 2020
No community comments so far
Editorial decision: Minor revision
On 27 Mar, 2021
Review #1 received
Received 25 Feb, 2021
Review #2 received
Received 25 Feb, 2021
Reviewer #2 agreed
On 14 Feb, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 13 Feb, 2021
Editor assigned
On 20 Dec, 2020
Submission checks complete
On 20 Dec, 2020
Editor invited
On 20 Dec, 2020
First submitted
On 18 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at ROBOMECH Journal.
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
This preprint is available for download as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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