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http://dx.doi.org/10.3745/KTSDE.2021.10.5.179

Evaluation of Human Demonstration Augmented Deep Reinforcement Learning Policies via Object Manipulation with an Anthropomorphic Robot Hand  

Park, Na Hyeon (경희대학교 전자정보융합공학과)
Oh, Ji Heon (경희대학교 전자정보융합공학과)
Ryu, Ga Hyun (경희대학교 전자정보융합공학과)
Lopez, Patricio Rivera (경희대학교 전자정보융합공학과)
Anazco, Edwin Valarezo (경희대학교 전자정보융합공학과)
Kim, Tae Seong (경희대학교 생체의공학과 및 전자정보융합공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.5, 2021 , pp. 179-186 More about this Journal
Abstract
Manipulation of complex objects with an anthropomorphic robot hand like a human hand is a challenge in the human-centric environment. In order to train the anthropomorphic robot hand which has a high degree of freedom (DoF), human demonstration augmented deep reinforcement learning policy optimization methods have been proposed. In this work, we first demonstrate augmentation of human demonstration in deep reinforcement learning (DRL) is effective for object manipulation by comparing the performance of the augmentation-free Natural Policy Gradient (NPG) and Demonstration Augmented NPG (DA-NPG). Then three DRL policy optimization methods, namely NPG, Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO), have been evaluated with DA (i.e., DA-NPG, DA-TRPO, and DA-PPO) and without DA by manipulating six objects such as apple, banana, bottle, light bulb, camera, and hammer. The results show that DA-NPG achieved the average success rate of 99.33% whereas NPG only achieved 60%. In addition, DA-NPG succeeded grasping all six objects while DA-TRPO and DA-PPO failed to grasp some objects and showed unstable performances.
Keywords
Anthropomorphic Robot Hand; Deep Reinforcement Learning; Human Demonstration; Policy Optimization;
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