Optimal grasping strategy using lmitation learning for pneumatic soft gripper = 이미테이션 러닝 기반 공압 소프트 그리퍼 파지 전략 최적화 연구

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Traditionally Robotic grasping is performed by a robot manipulator with a rigid end-effector (a hard gripper). The usage of such kind of end effectors requires a high accuracy object detection to detect the best candidate contact points and precise control for the motion planning to perform a successful grasp. Additionally, force control is needed to prevent damage to the target object, especially if it is fragile. On the other hand, grippers made of soft materials shown to be compliant with different object shapes and can perform a safe and stable grasping due to the nature of soft material. In this thesis, we propose the implementation of an imitation learning algorithm to simplify the control scheme, by combining with a pneumatic soft gripper. As a result, the system proposed exploits such advantages of soft grippers so the limitations of a hard gripper based system can be solved. We compare the performance of the model to the demonstrations collected by changing the position, pose, and shape of two different target objects, and additionally, we tested the grasping successful rate.
Advisors
Kyung, Ki Ukresearcher경기욱researcher
Description
한국과학기술원 :로봇공학학제전공,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2020.8,[vi, 49 p. :]

Keywords

Expert Demonstration▼aGrasping Strategy▼aImitation Learning▼aNeural Network▼aPolicy▼aSoft Gripper; 전문가 데모▼a파지 전략▼a이미테이션 러닝▼a신경망▼a정책▼a소프트 그리퍼

URI
http://hdl.handle.net/10203/284963
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925122&flag=dissertation
Appears in Collection
RE-Theses_Master(석사논문)
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