Hojung Choi

Hello! I am Hojung, a postdoc working in the Robotics and Embodied Artificial Intelligence Lab advised by Professor Shuran Song, and jointly advised by Professor Mark Cutkosky. My research interests are in designing new hardware and algorithms for contact-rich manipulation.

Prior to starting postdoc, I completed my PhD and MS in Mechanical Engineering at Stanford University where I was advised by Professor Mark Cutkosky in the Biomimetics and Dexterous Manipulation Lab.

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

Hojung Choi*, Jun En Low*, Tae Myung Huh, Gabriela Uribe, Seongheon Hong, Kenneth Hoffmann, Julia Di, Tony Chen, Andrew Stanley, and Mark Cutkosky

CoinFT is a compact, low-cost, and robust 6-axis force/torque sensor made of stacked PCBs and silicone pillars with comb-shaped electrodes. Its design enables sensitive force measurements while maintaining a small form factor. CoinFT supports diverse contact-rich interactions across platforms like drones, robot end-effectors, and wearable devices. We demonstrate its utility through drone-based force-controlled tasks requiring precise contact modulation.

[Open-source] [arXiv]

DexForce: Extracting Force-Informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation

Claire Chen, Zhongchun Yu, Hojung Choi, Mark Cutkosky, Jeannette Bohg

We present DexForce, a method for collecting contact-rich dexterous manipulation demonstrations using kinesthetic teaching with force measurements. By incorporating contact forces into action computation, DexForce enables effective imitation learning, achieving a 76% average success rate across six tasks—significantly outperforming force-agnostic baselines, especially in tasks requiring high precision.

[Project Website] [arXiv]

Additively Manufactured Micro-lattice Dielectrics for Multiaxial Capacitive Sensors

Arielle Berman, Kaiwen Hsiao, Samuel E. Root, Hojung Choi, Daniel Ilyn, Chengyi Xu, Emily Stein, Mark Cutkosky, Joseph M. DeSimone, and Zhenan Bao

Science Advances, 2024

The paper presents the development of additively manufactured micro-lattice structures used as dielectric layers in multiaxial capacitive sensors. These sensors, created using continuous liquid interface production (CLIP), exhibit tunable responses under both normal pressure and shear forces, making them suitable for applications in wearable electronics, robotic manipulation, and athletic performance monitoring.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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.

[PDF]

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