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The orange points represent grasping position candidates.
"Grasping position detection" and "Grasping motion determination" sections explains the proposed method.
First, 30 pieces of learning data for grasping position determination were prepared.
Points without filling are the original grasping position linked to the learning data and another point with filling is the grasping position with respect to the input data newly obtained by the alignment process.
It included the detection of the thickest holded hem, determination of the grasping position, and determination of via/grasping posture.
We propose a method to determine the grasping position from a folded cloth product placed on a table.
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These instruments require the use of hook grasp position which differentiates from the other types of power grasp in terms of inclusion of the thumb.
Then, we propose methods of grasp position estimation composed of two stages: detection of the thickest folded hem and pose estimation of the cloth product.
How to detect a grasped position from a folded cloth product: since the shape of the cloth has a certain regularity, it is relatively easy to detect a hem portion as a border.
The door opening task can be described using parent-child relationship of the door pivot, the door lever pivot, and target hand poses as grasp position on the door lever.
This problem setting is pretty simple but includes outstanding issues as follows: How to detect a grasped position from a folded cloth product: since the shape of the cloth has a certain regularity, it is relatively easy to detect a hem portion as a border.
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