John (Chung Hee) Kim

We enable a multi-fingered robot to operate entirely by feel in confined spaces where vision fails, exploring through contact to discover objects and identifying them by reconstructing their shape.

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.

A system and method for accurately placing or inserting objects using robotic manipulation is disclosed.