Citation

Mohammed, Marwan Qaid and Kwek, Lee Chung and Chua, Shing Chyi (2021) Learning Pick to Place Objects using Self-supervised Learning with Minimal Training Resources. International Journal of Advanced Computer Science and Applications, 12 (10). ISSN 2158-107X

Text Learning Pick to Place Objects using Self supervised....pdf Restricted to Repository staff only Download (802kB)

Abstract

Grasping objects is a critical but challenging aspect of robotic manipulation. Recent studies have concentrated on complex architectures and large, well-labeled data sets that need extensive computing resources and time to achieve generalization capability. This paper proposes an effective grasp-to-place strategy for manipulating objects in sparse and chaotic environments. A deep Q-network, a model-free deep reinforcement learning method for robotic grasping, is employed in this paper. The proposed approach is remarkable in that it executes both fundamental object pickup and placement actions by utilizing raw RGB-D images through an explicit architecture. Therefore, it needs fewer computing processes, takes less time to complete simulation training, and generalizes effectively across different object types and scenarios. Our approach learns the policies to experience the optimal grasp point via trial-and-error. The fully conventional network is utilized to map the visual input into pixel-wise Q-value, a motion agnostic representation that reflects the grasp's orientation and pose. In a simulation experiment, a UR5 robotic arm equipped with a Parallel-jaw gripper is used to assess the proposed approach by demonstrating its effectiveness. The experimental outcomes indicate that our approach successfully grasps objects with consuming minimal time and computer resources.

Item Type:	Article
Uncontrolled Keywords:	Self-supervised, pick-to-place, robotics, deep q-network
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	17 Jan 2022 10:18
Last Modified:	17 Jan 2022 10:18
URII:	http://shdl.mmu.edu.my/id/eprint/9849

Downloads

Edit (login required)