Citation

Al-Selwi, Hatem Fahd and Abd. Aziz, Azlan and Abas, Fazly Salleh and Zyada, Zakarya (2021) Reinforcement Learning for Robotic Applications with Vision Feedback. In: 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA), 5-6 March 2021, Langkawi, Malaysia.

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Abstract

The presence of robots in our daily life is becoming more common, where robots start carrying out more complex tasks. This increase in the complexity of tasks makes conventional control system insufficient. Therefore, a plausible approach is required for robots to learn how to perform these tasks. Reinforcement learning enables robots to perform complex tasks without highly engineered control systems. However, using reinforcement learning in robotic applications is challenged by several problems such as high dimensionality. Thus, in this paper, we study the performance of the Hindsight Experience Replay (HER) algorithm which addresses the high dimensionality problem. In this paper, we analyze the algorithm performance using a simulated robotic arm to pick and place different objects. Then, we propose the use of vision feedback which is used to control the gripper of the robotic arm. The results and analysis highlights some of HER limitations when dealing with objects that have limited grasping points. Our proposed method allows the robotic arm to pick objects using the same trained policy without the need to retrain the agent for new objects. Finally, we prove that using our method the robotic arm can pick the objects with higher success rate compared to the one without vision feedback.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Reinforcement learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	25 Apr 2021 19:37
Last Modified:	25 Apr 2021 19:37
URII:	http://shdl.mmu.edu.my/id/eprint/8643

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