John (Chung Hee) Kim
I am a PhD candidate in the Robotics Institute at Carnegie Mellon University. I work at the Kantor Lab advised by Dr. George Kantor. I was an Applied Scientist Intern at Amazon Robotics where I worked with Dr. Joshua Migdal and Dr. Taskin Padir.
I received my B.Eng. in Mechanical Engineering and M.Phil. in Electronic and Computer Engineering from the Hong Kong University of Science and Technology, where I worked as a research assistant in the Robot Manipulation Lab advised by Dr. Jungwon Seo.
Research
My research interest lies in the intersection of robotic manipulation, perception, and machine learning. I enjoy solving real world problems by working with robots to develop practical systems and solutions.
We develop a tactile-sensing system that reconstructs 3D object geometry through touch alone, showing that 'how' a robot makes contact matters just as much as 'where'.
Autonomous robotic pepper harvesting in the wild!
We present a framework for learning robotic contact manipulation of tree-like crops by leveraging graph representations.
We develop a computer vision method to extract skeleton representation of trees from images featuring large amounts of foliage and self-occlusion.
We develop a method for creating 3D models of sorghum panicles and a non-destructive approach to estimate seed count and weight.
This enables human-level dexterity in the tasks of ungrasping, e.g., the placement of Go stones that AlphaGo is unable to perform.
There's more to our Dexterous Ungrasping — robustness.
Effective bin picking using an asymmetric gripper with different finger lengths.
We present an effective manipulation technique for picking thin objects from a flat surface.
We address how to insert a thin object (such as a phone battery) into a shallow-depth hole through dexterous manipulation.
Patents and Copyrights
A system and method for accurately placing or inserting objects using robotic manipulation is disclosed.