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pro vyhledávání: '"Kim, Sangwoon"'
Autor:
Kim, Sangwoon
This thesis introduces the concept of TEXterity (Tactile Extrinsic deXterity) to address challenges in robotic manipulation. Focusing on tactile sensing, TEXterity aims to enhance dexterity by enabling robots to perceive and act upon extrinsic contac
Externí odkaz:
https://hdl.handle.net/1721.1/155651
We introduce a novel approach that combines tactile estimation and control for in-hand object manipulation. By integrating measurements from robot kinematics and an image-based tactile sensor, our framework estimates and tracks object pose while simu
Externí odkaz:
http://arxiv.org/abs/2403.00049
We introduce a novel approach that combines tactile estimation and control for in-hand object manipulation. By integrating measurements from robot kinematics and an image-based tactile sensor, our framework estimates and tracks object pose while simu
Externí odkaz:
http://arxiv.org/abs/2401.10230
We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling light forces and accurate motions in contact, such as balancing an unkn
Externí odkaz:
http://arxiv.org/abs/2303.03385
Autor:
Kim, Sangwoon, Rodriguez, Alberto
We propose a method that actively estimates contact location between a grasped rigid object and its environment and uses this as input to a peg-in-hole insertion policy. An estimation model and an active tactile feedback controller work collaborative
Externí odkaz:
http://arxiv.org/abs/2110.03555
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
Cataloged from the PDF version of thesis.
Includes bibliographical references (pages 73-76).
A deep reinforcement learning (DRL) appro
Cataloged from the PDF version of thesis.
Includes bibliographical references (pages 73-76).
A deep reinforcement learning (DRL) appro
Externí odkaz:
https://hdl.handle.net/1721.1/132901
Autor:
Dong, Siyuan, Jha, Devesh K., Romeres, Diego, Kim, Sangwoon, Nikovski, Daniel, Rodriguez, Alberto
Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly limits the ability to understand the contact configuration between the ob
Externí odkaz:
http://arxiv.org/abs/2104.01167
This paper presents a model-free deep reinforcement learning (DRL) approach for controlling a fiber drawing system. The custom DRL-based control system predictively regulates fiber diameter and produces a fiber with a desired, constant or non-constan
Externí odkaz:
http://arxiv.org/abs/1911.10286
Autor:
Kang, Ha Eun David, Kim, Duhyeong, Kim, Sangwoon, Kim, David Donghyun, Cheon, Jung Hee, Anthony, Brian W. *
Publikováno v:
In Journal of Manufacturing Systems October 2021 61:856-865
Akademický článek
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