Na Hyeon Park, Ji Heon Oh, Ga Hyun Ryu, Patricio Rivera Lopez, Edwin Valarezo Añazco, Tae Seong Kim

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 5, pp. 179-186, May. 2021
https://doi.org/10.3745/KTSDE.2021.10.5.179,   PDF Download:
Keywords: Anthropomorphic Robot Hand, Deep Reinforcement Learning, Human Demonstration, Policy Optimization
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.

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