7 citations · 17 across the 7 of their papers we have counts for
7 papers
Practical Insights on Grasp Strategies for Mobile Manipulation in the Wild
Isabella Huang, Richard Cheng, Sangwoon Kim +8
Mobile manipulation robots are continuously advancing, with their grasping capabilities rapidly progressing. However, there are still significant gaps preventing state-of-the-art m…
TEXterity -- Tactile Extrinsic deXterity: Simultaneous Tactile Estimation and Control for Extrinsic Dexterity
Sangwoon Kim, Antonia Bronars, Parag Patre +1
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 tac…
TEXterity: Tactile Extrinsic deXterity
Antonia Bronars, Sangwoon Kim, Parag Patre +1
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 tac…
Simultaneous Tactile Estimation and Control of Extrinsic Contact
Sangwoon Kim, Devesh K. Jha, Diego Romeres +2
We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling l…
Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion
Sangwoon Kim, Alberto Rodriguez
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 estimat…
Tactile-RL for Insertion: Generalization to Objects of Unknown Geometry
Siyuan Dong, Devesh K. Jha, Diego Romeres +3
Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly lim…
Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning
Sangwoon Kim, David Donghyun Kim, Brian Anthony
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…