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현실 세계에서의 로봇 파지 작업을 위한 정책/가치 심층 강화학습 플랫폼 개발

Title
현실 세계에서의 로봇 파지 작업을 위한 정책/가치 심층 강화학습 플랫폼 개발
Other Titles
Development of an Actor-Critic Deep Reinforcement Learning Platform for Robotic Grasping in Real World
Author
서일홍
Keywords
Actor-Critic Deep Reinforcement Learning; Robotic Grasping
Issue Date
2020-05
Publisher
한국로봇학회
Citation
로봇학회 논문지, v. 15, no. 2, page. 197-204
Abstract
In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.
URI
http://jkros.org/_common/do.php?a=full&b=33&bidx=2193&aidx=26094https://repository.hanyang.ac.kr/handle/20.500.11754/166663
ISSN
1975-6291; 2287-3961
DOI
10.7746/jkros.2020.15.2.197
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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