Citation

Mohammed, Marwan Qaid and Kwek, Lee Chung and Chua, Shing Chyi (2021) Robotic Grasping In Clutter And Occlusion With Deep Reinforcement Learning. In: 2nd FET PG Engineering Colloquium Proceedings 2021, 1-15 Dec. 2021, Online Conference. (Unpublished)

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Abstract

The first proposed PDDBA approach contains two main components that handle the above-mentioned issues: 1) leveraging a single Fully Connected Network to predict the best pushing and grasping pose, and 2) using a pixel-depth difference-based synergizing the execution of a push and grasp action. Whereas, the second proposed MV-COBA approach is divided into two parts: 1) using multiple cameras to set up multi-view to address the occlusion issue while also improving grasp performance in cluttered and occluded environments, increasing the likelihood of a successful grasp.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Depth Difference, Multi-View, Change Observation, Synergizing Two Actions, Deep-RL, Robotic Grasping, Cluttered Scene
Subjects:	T Technology > TJ Mechanical Engineering and Machinery
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	26 Jan 2022 04:08
Last Modified:	26 Jan 2022 04:08
URII:	http://shdl.mmu.edu.my/id/eprint/9912

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