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A Deep Learning-based Grasp Pose Estimation Approach for Large-Size Deformable Objects in Clutter | IEEE Conference Publication | IEEE Xplore
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A Deep Learning-based Grasp Pose Estimation Approach for Large-Size Deformable Objects in Clutter


Abstract:

Deformable objects especially large-size de-formable objects grasping is unappreciated but widespread in industrial applications (e.g., clothes recycling). While it encou...Show More

Abstract:

Deformable objects especially large-size de-formable objects grasping is unappreciated but widespread in industrial applications (e.g., clothes recycling). While it encounters several challenges, for example, the existing methods didn’t take large-size deformable objects into account, no typical boundary of deformable objects. To solve the challenges, we proposed a grasp detection framework consisting of a self-trained object detection network, an instance segmentation module, and a grasp pose generation pipeline. The experiments were successfully conducted on the industrial laundry mock-up with an 88.9% success ratio. The experiments result indicates the effectiveness of the proposed framework on spatial-constrained large-size deformable objects grasping in clutter.
Date of Conference: 26-30 August 2024
Date Added to IEEE Xplore: 30 October 2024
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Conference Location: Pasadena, CA, USA

References

References is not available for this document.