Hojung Choi
Hello! I am Hojung, a PhD student in the Biomimetics and Dexterous Manipulation Laboratory (BDML) at Stanford University. My research interests are in the design and use of novel tactile sensors for robots and wearable haptic devices.
Prior to starting PhD, I completed my MS in Mechanical Engineering at Stanford University and a BS in Mechanical Engineering with a minor in Business Administration at the Ulsan National Institute of Science and Technology (UNIST) in South Korea, where I had the fortune of working in the Bio-Robotics and Controls Lab advised by Professor Joonbum Bae.
I also love cooking and inviting people. In the coming years you might find me in the food-tech space!
email: hjchoi92@stanford.edu / ilovehjb0520@gmail.com
Research
CoinFT: A Coin-Sized 6-Axis Force Torque Sensor for Robotic Applications
Coming Soon!
Integrated Pneumatic Sensing and Actuation for Soft Haptic Devices
Hojung Choi, Mark R Cutkosky, Andrew A Stanley
IEEE Robotics and Automation Letters, 2023
We present an integrated pneumatic sensor and actuator for soft robots and wearable devices that can estimate contact force, location, and shape using a multi-headed neural network. The sensor uses channels cast into a multi-layer silicone bubble that change flow resistance as the bubble deforms, allowing measurements of strain and external contact at the surface.
Deep Learning Classification of Touch Gestures Using Distributed Normal and Shear Force
Hojung Choi, Dane Brouwer, Michael A Lin, Kyle T Yoshida, Carine Rognon, Benjamin Stephens-Fripp, Allison M Okamura, Mark R Cutkosky
IEEE International Conference on Intelligent Robots and Systems (IROS), 2022
Best Poster Presentation Award in IROS Workshop on Large-Scale Robotic Skin: Perception, Interaction, and Control
When humans socially interact with another agent (e.g., human, pet, or robot) through touch, they do so by applying varying amounts of force with different directions, locations, contact areas, and durations. We present a soft, flexible skin with an array of tri-axial tactile sensors for the arm of a person or robot. We demonstrate that shear force information makes a notable difference in touch gesture classification.
Tactile-Informed Action Primitives Mitigate Jamming in Dense Clutter
Dane Brouwer, Joshua Citron, Hojung Choi, Marion Lepert, Michael Lin, Jeannette Bohg, Mark Cutkosky
IEEE International Conference on Robotics and Automation (ICRA), 2024, Accepted
It is difficult for robots to retrieve objects in densely cluttered lateral access scenes with movable objects as jamming against adjacent objects and walls can inhibit progress. We propose the use of two action primitives - burrowing and excavating - that can fluidize the scene to unjam obstacles and enable continued progress. We combine the primitives into a closed loop hybrid strategy using tactile and proprioceptive information to leverage the advantages of both primitives without being overly disruptive.
Design and Evaluation of a 3-DoF Haptic Device for Directional Shear Cues on the Forearm
Kyle T Yoshida, Zane A Zook, Hojung Choi, Ming Luo, Marcia K O'Malley, Allison M Okamura
IEEE Transactions on Haptics, 2024, Accepted
Wearable haptic devices on the forearm can relay information from virtual agents, robots, and other humans while leaving the hands free. We introduce and test a new wearable haptic device that uses soft actuators to provide normal and shear force to the skin of the forearm.
Perceived Intensities of Normal and Shear Skin Stimuli Using a Wearable Haptic Bracelet
Mine Sarac, Taemyung Huh, Hojung Choi, Mark R Cutkosky, Massimiliano Di Luca, Allison M Okamura
IEEE Robotics and Automation Letters, 2022
We aim to provide effective interaction with virtual objects, despite the lack of co-location of virtual and real-world contacts, while taking advantage of relatively large skin area and ease of mounting on the forearm. We performed two human participant studies to determine the effects of haptic feedback in the normal and shear directions during virtual manipulation using haptic devices worn near the wrist.
Exploratory Hand: Leveraging Safe Contact to Facilitate Manipulation in Cluttered Spaces
Michael A Lin, Rachel Thomasson, Gabriela Uribe, Hojung Choi, Mark Cutkosky
IEEE Robotics and Automation Letters, 2021
We present a new gripper and exploration approach that uses a finger with very low reflected inertia for probing and then grasping objects. The finger employs a transparent transmission, resulting in a light touch when contact occurs.
Dynamically Reconfigurable Tactile Sensor for Robotic Manipulation
Taemyung Huh, Hojung Choi, Simone Willcox, Stephanie Moon, Mark R Cutkosky
IEEE Robotics and Automation Letters, 2020
We present a tactile sensor intended for manipulation by mobile robots, e.g., in the home. The surface consists of an array of small, rounded bumps or "nibs", which transduce 6-axis force, torque, and slippage into distinguishable capacitance signals. This information can be used for dexterous manipulation tasks such as in-hand object reorientation.
Using Force Data to Self-Pace an Instrumented Treadmill and Measure Self-Selected Walking Speed
Seungmoon Song, Hojung Choi, Steve H Collins
Journal of NeuroEngineering and Rehabilitation, 2020
Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed using an instrumented treadmill was developed to measure self-selected walking speed.
Jumping Further: Forward Jumps in a Gravity-Reduced Immersive Virtual Environment
Hyeongyeop Kang, Geonsun Lee, Daeseok Kang, Ohung Kwon, Junyeup Cho, Hojung Choi, Junghyun Han
IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2019
Best Paper Finalist
In this work, we investigate how to simulate realistic forward jumps in a virtual lunar environment using a cable-driven suspension system.