Won Kyung Do (도원경, 都源京)

I'm a fifth-year Ph.D. Candidate in Mechanical Engineering at Stanford University. I am advised by Prof. Monroe Kennedy III in ARM Lab.

Before coming to Stanford, I received B.S. in Mechanical Engineering from Seoul National University, advised by Prof. Dongjun Lee. I was fortunate to work as a research intern at Naver Labs in 2018.

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Research

I'm interested in tactile sensing, dexterous manipulation, small object manipulation, control theory, optimization, and human-robot interaction. I believe improving dexterity of the robots is essential for enabling them to perform precise tasks such as manipulating small objects autonomously. Representative papers are highlighted.

Developed a novel optical tactile sensor that can grasp multi-scale objects from flat surfaces (from 1mm basil seeds and small paperclips to items nearly 15mm).

Describes a method that uses optical tactile sensing for efficient detection and mapping of objects embedded in soft materials, demonstrated with quartz beads beneath polyethylene foam.

Presents a novel method that enhances 3D Gaussian Splatting with optical tactile sensors for more accurate object representation in robotics, integrating tactile data with monocular depth images and a new variance-weighted loss function for improved scene synthesis across various materials.

Introduces a tactile sensor-equipped gripper with DenseTact 2.0, enhancing precision and success in grasping small objects in cluttered environments, integrated with a specialized control algorithm and dataset for effective object classification and manipulation.

Designed DenseTact 2.0, an advanced optical-tactile sensor that accurately reconstructs shapes and measures forces in robotic fingertips with smaller form factors and higher resolution than previous models.

Introduces Densetact, a cost-effective, high-resolution tactile sensor that uses a fisheye camera and deep neural networks for precise 3D surface modeling in real-time.

Integrates sensor gloves and stereo cameras with a fusion algorithm for precise and reliable hand tracking, improving performance in real-world applications despite challenges like occlusions and electromagnetic interference.

Introduces a framework combining multi-modal tactile sensing and modular design with a classifier for recognizing social touch patterns, achieving up to 88.86% accuracy using methods like HMM, LSTM, and 3D-CNN.

Showed that the robot Shelly effectively reduces children's robot abusing while maintaining their engagement with the robot.

Designed "Shelly", a tortoise-like robot that engages with children while mitigating abusive behaviors towards robots.

Service

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