Approaching Collaborative Manipulation by Pushing-Grasping Fusion
Smart manipulation is always the desired performance for the interesting researches in the field of robotic control. The complicated fusion among motion primitives could offer the advanced adaptions in presence of highly success rate or unknown objects. In this paper, a hierarchical framework for pu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11016017/ |
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| author | Lu Anh Duy Phan Dang Quy Phan The Tri Bui Phuong H. Le Dinh Tuan Tran Joo-Ho Lee Ha Quang Thinh Ngo |
| author_facet | Lu Anh Duy Phan Dang Quy Phan The Tri Bui Phuong H. Le Dinh Tuan Tran Joo-Ho Lee Ha Quang Thinh Ngo |
| author_sort | Lu Anh Duy Phan |
| collection | DOAJ |
| description | Smart manipulation is always the desired performance for the interesting researches in the field of robotic control. The complicated fusion among motion primitives could offer the advanced adaptions in presence of highly success rate or unknown objects. In this paper, a hierarchical framework for pushing-grasping fusion in the cluttered environment is demonstrated. Our method involves three-phase training process, integration of masks and the reinforcement learning scheme. Both simulation and experimentation are undertaken to validate the efficacy and feasibility of the proposed methodology. Our contributions in this work are (i) to propose both grasp mask and push mask for encouraging the model to focus on exploiting, adjusting the proper grasping posture in the desired target area as well as avoiding the phenomenon of the gripper slipping on the surface of an object, (ii) to recommend the reinforcement learning scheme without object model, and (iii) to release a hierarchical training method to enhance the interactive efficiency between grasping and pushing actions. |
| format | Article |
| id | doaj-art-1380745375644bf6a33d880fa6a667bc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1380745375644bf6a33d880fa6a667bc2025-08-20T03:44:57ZengIEEEIEEE Access2169-35362025-01-0113976939770710.1109/ACCESS.2025.357401811016017Approaching Collaborative Manipulation by Pushing-Grasping FusionLu Anh Duy Phan0Dang Quy Phan1https://orcid.org/0000-0001-6360-6446The Tri Bui2https://orcid.org/0000-0001-6350-3172Phuong H. Le3Dinh Tuan Tran4https://orcid.org/0000-0001-7443-9102Joo-Ho Lee5https://orcid.org/0000-0003-1015-5615Ha Quang Thinh Ngo6https://orcid.org/0000-0002-7898-1107Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamVietnam National University-Ho Chi Minh City (VNU-HCM),Thu Duc City, Ho Chi Minh City, VietnamFaculty of Data Science, Shiga University, Hikone, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kyoto, JapanFaculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamSmart manipulation is always the desired performance for the interesting researches in the field of robotic control. The complicated fusion among motion primitives could offer the advanced adaptions in presence of highly success rate or unknown objects. In this paper, a hierarchical framework for pushing-grasping fusion in the cluttered environment is demonstrated. Our method involves three-phase training process, integration of masks and the reinforcement learning scheme. Both simulation and experimentation are undertaken to validate the efficacy and feasibility of the proposed methodology. Our contributions in this work are (i) to propose both grasp mask and push mask for encouraging the model to focus on exploiting, adjusting the proper grasping posture in the desired target area as well as avoiding the phenomenon of the gripper slipping on the surface of an object, (ii) to recommend the reinforcement learning scheme without object model, and (iii) to release a hierarchical training method to enhance the interactive efficiency between grasping and pushing actions.https://ieeexplore.ieee.org/document/11016017/Deep learningrobotic manipulationpushing-grasping fusionMarkov decision processvisual pushing and grasping |
| spellingShingle | Lu Anh Duy Phan Dang Quy Phan The Tri Bui Phuong H. Le Dinh Tuan Tran Joo-Ho Lee Ha Quang Thinh Ngo Approaching Collaborative Manipulation by Pushing-Grasping Fusion IEEE Access Deep learning robotic manipulation pushing-grasping fusion Markov decision process visual pushing and grasping |
| title | Approaching Collaborative Manipulation by Pushing-Grasping Fusion |
| title_full | Approaching Collaborative Manipulation by Pushing-Grasping Fusion |
| title_fullStr | Approaching Collaborative Manipulation by Pushing-Grasping Fusion |
| title_full_unstemmed | Approaching Collaborative Manipulation by Pushing-Grasping Fusion |
| title_short | Approaching Collaborative Manipulation by Pushing-Grasping Fusion |
| title_sort | approaching collaborative manipulation by pushing grasping fusion |
| topic | Deep learning robotic manipulation pushing-grasping fusion Markov decision process visual pushing and grasping |
| url | https://ieeexplore.ieee.org/document/11016017/ |
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