About

Joohyung Kim is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Illinois. He serves as the director of KIMLAB, the Kinetic Intelligent Machine LAB. His research focuses on the development and application of intelligent machine systems, with an emphasis on kinetic and robotic technologies. Dr. Kim's work involves advancing the understanding and engineering of intelligent, dynamic systems, contributing to the fields of robotics and electrical engineering through innovative research and leadership.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Psychology
• Business
• Surgery
• Engineering
• Computer graphics (images)
• Social psychology
• Database
• Marketing
• Medicine
• Human–computer interaction
• Process management
• Cognitive psychology
• Computer vision

Selected publications

• Self-rising Bipedal Robot for Embracing Fall Impact and Fall Detection with Multimodal Sensing

  2026-01-11

  articleSenior author

  Humanoid robots are inherently unstable, making fall management critical, especially for hardware-based reinforcement learning (RL), where falls frequently occur. This paper introduces a lantern-shaped mechanical cover designed for a kid-sized humanoid robot to mitigate damage during falls and support autonomous recovery. A multimodal fall detection method integrating inertial, proprioceptive, and acoustic sensors was implemented alongside an improved stance phase detection algorithm that eliminates reliance on heuristic thresholds. Hardware experiments on the Hybrid Leg biped robot demonstrated improved walking robustness and revealed a 57.4% success rate for autonomous recovery after induced falls. Results indicated that perturbations in vertical (z) and positive forward (x) foot trajectories posed the greatest challenges to successful recovery.

  PublisherDOI

• TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders

  arXiv (Cornell University) · 2026-03-10

  articleOpen accessSenior author

  Large scale, diverse demonstration data for manipulation tasks remains a major challenge in learning-based robot policies. Existing in-the-wild data collection approaches often rely on vision-based pose estimation of hand-held grippers or gloves, which introduces an embodiment gap between the collection platform and the target robot. Teleoperation systems eliminate the embodiment gap, but are typically impractical to deploy outside the laboratory environment. We propose TRIP-Bag (Teleoperation, Recording, Intelligence in a Portable Bag), a portable, puppeteer-style teleoperation system fully contained within a commercial suitcase, as a practical solution for collecting high-fidelity manipulation data across varied settings. With a setup time of under five minutes and direct joint-to-joint teleoperation, TRIP-Bag enables rapid and reliable data collection in any environment. We validated TRIP-Bag's usability through experiments with non-expert users, showing that the system is intuitive and easy to operate. Furthermore, we confirmed the quality of the collected data by training benchmark manipulation policies, demonstrating its value as a practical resource for robot learning.

  PublisherOA PDF

• Motion Design for Grasp-Based Dynamic Locomotion in Microgravity

  arXiv (Cornell University) · 2026-05-20

  preprintOpen access

  Locomotion in microgravity often relies on sparsely and irregularly arranged anchors, motivating grasp-based mobility with multiple limbs. In this setting, dynamic locomotion is feasible only through deliberate regulation of both anchored interactions and whole-body coordination under coupled dynamic and kinematic constraints. This paper presents design insights for grasp-based dynamic locomotion with multi-limbed robotic systems in microgravity, targeting scenarios that require 6D limb manipulation to establish contacts with candidate anchors. The investigated design parameters include gait pattern, stride length, locomotion speed, and nominal posture. A parameterizable locomotion planning framework is proposed to support variations of these parameters and to evaluate the resulting locomotion performance in terms of stability and actuation demand. Two representative quadruped morphologies are adopted for evaluation in physics-based simulation. The results demonstrate that enlarging the feasible contact wrench space and attenuating impulsive whole-body dynamics improve locomotion performance. These findings inform strategies for contact configuration selection and whole-body coordination in microgravity locomotion with multi-limbed systems.

  PublisherDOI

• Motion Design for Grasp-Based Dynamic Locomotion in Microgravity

  ArXiv.org · 2026-05-20

  articleOpen access

  Locomotion in microgravity often relies on sparsely and irregularly arranged anchors, motivating grasp-based mobility with multiple limbs. In this setting, dynamic locomotion is feasible only through deliberate regulation of both anchored interactions and whole-body coordination under coupled dynamic and kinematic constraints. This paper presents design insights for grasp-based dynamic locomotion with multi-limbed robotic systems in microgravity, targeting scenarios that require 6D limb manipulation to establish contacts with candidate anchors. The investigated design parameters include gait pattern, stride length, locomotion speed, and nominal posture. A parameterizable locomotion planning framework is proposed to support variations of these parameters and to evaluate the resulting locomotion performance in terms of stability and actuation demand. Two representative quadruped morphologies are adopted for evaluation in physics-based simulation. The results demonstrate that enlarging the feasible contact wrench space and attenuating impulsive whole-body dynamics improve locomotion performance. These findings inform strategies for contact configuration selection and whole-body coordination in microgravity locomotion with multi-limbed systems.

  PublisherOA PDF

• PAPRLE: Plug-And-Play Robotic Limb Environment: A Modular Ecosystem for Robotic Limbs

  IEEE Robotics & Automation Magazine · 2026-01-19

  articleOpen accessSenior author

  We introduce PAPRLE (plug-and-play robotic limb environment), a modular ecosystem that enables flexible placement and control of robotic limbs. With PAPRLE a user can change the arrangement of the robotic limbs and control them using a variety of input devices, including puppeteers, gaming controllers, and virtual reality (VR) devices. This versatility supports a wide range of teleoperation scenarios and promotes adaptability to different task requirements. We also introduce a pluggable puppeteer device that can be easily mounted and adapted to match the target robot configurations. PAPRLE supports bilateral teleoperation through these puppeteer devices, agnostic to the type or configuration of the follower robot. The modular design of PAPRLE facilitates novel spatial arrangements of the limbs and enables scalable data collection, thereby advancing research in embodied artificial intelligence (AI) and learning-based control. We validate PAPRLE in various real-world settings, demonstrating its versatility across diverse combinations of leader devices and follower robots. The system will be released as open source, including both hardware and software components, to support broader adoption and extension.

  PublisherDOI

• TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders

  arXiv (Cornell University) · 2026-03-10

  preprintOpen accessSenior author

  Large scale, diverse demonstration data for manipulation tasks remains a major challenge in learning-based robot policies. Existing in-the-wild data collection approaches often rely on vision-based pose estimation of hand-held grippers or gloves, which introduces an embodiment gap between the collection platform and the target robot. Teleoperation systems eliminate the embodiment gap, but are typically impractical to deploy outside the laboratory environment. We propose TRIP-Bag (Teleoperation, Recording, Intelligence in a Portable Bag), a portable, puppeteer-style teleoperation system fully contained within a commercial suitcase, as a practical solution for collecting high-fidelity manipulation data across varied settings. With a setup time of under five minutes and direct joint-to-joint teleoperation, TRIP-Bag enables rapid and reliable data collection in any environment. We validated TRIP-Bag's usability through experiments with non-expert users, showing that the system is intuitive and easy to operate. Furthermore, we confirmed the quality of the collected data by training benchmark manipulation policies, demonstrating its value as a practical resource for robot learning.

  PublisherDOI

• CoRe: A Hybrid Approach of Contact-Aware Optimization and Learning for Humanoid Robot Motions

  2025-09-30

  article

  Recent advances in text-to-motion generation enable realistic human-like motions directly from natural language. However, translating these motions into physically executable motions for humanoid robots remains challenging due to significant embodiment differences and physical constraints. Existing methods primarily rely on reinforcement learning (RL) without addressing initial kinematic infeasibility. This often leads to unstable robot behaviors. We introduce Contactaware motion Refinement (CoRe), a fully automated pipeline consisting of human motion generation from text, robot-specific retargeting, optimization-based motion refinement, and a subsequent RL phase enhanced by contact-aware rewards. This integrated approach mitigates common motion artifacts such as foot sliding, unnatural floating, and excessive joint accelerations prior to RL training, thereby improving overall motion stability and physical plausibility. We validate our pipeline across diverse humanoid platforms without task-specific tuning or dynamiclevel optimization. Results demonstrate effective sim-to-real transferability in various scenarios, from simple upper-body gestures to complex whole-body locomotion tasks.

  PublisherDOI

• Robust and Expressive Humanoid Motion Retargeting via Optimization-Based Rig Unification

  2025-10-19

  article

  Humanoid robots are increasingly being developed for seamless interaction with humans in diverse domains, yet generating expressive and physically-feasible motions remains a core challenge. We propose a robust and automated pipeline for motion retargeting that enables the generation of natural, stable, and highly expressive motions for a wide variety of humanoid robots using different motion data sources, including noisy pose estimations. To ensure robustness, our approach unifies motions from different kinematic structures into a common canonical rig, systematically refines the motion trajectory to address infeasible poses, enforces foot-contact constraints, and enhances stability. The retargeted motion is then refined to closely follow the source motion while respecting each robot’s physical limits. Through extensive experiments on 12 simulated robots and validation on three real robots, we show that our methodology reliably produces expressive upper-body movements with consistent foot contact. This work represents an important step towards automating robust and expressive motion generation for humanoid robots, enabling deployment in various real-world scenarios.

  PublisherDOI

• Coordinated Motion Planning of a Wearable Multi-Limb System for Enhanced Human-Robot Interaction

  ArXiv.org · 2025-09-12

  preprintOpen accessSenior author

  Supernumerary Robotic Limbs (SRLs) can enhance human capability within close proximity. However, as a wearable device, the generated moment from its operation acts on the human body as an external torque. When the moments increase, more muscle units are activated for balancing, and it can result in reduced muscular null space. Therefore, this paper suggests a concept of a motion planning layer that reduces the generated moment for enhanced Human-Robot Interaction. It modifies given trajectories with desirable angular acceleration and position deviation limits. Its performance to reduce the moment is demonstrated through the simulation, which uses simplified human and robotic system models.

  PublisherOA PDFDOI

• A Study on the Irish Citizens’ Assembly: Democratic Innovations, Deliberative Democracy 4.0, and Citizens’ Assembly

  Journal of Korean Politics · 2025-02-28

  articleSenior author

  시민의회는 전통적인 대의민주주의의 한계를 보완 및 쇄신하기 위한 민주적 혁신의 대표적인 유형이다. 그중에서도 아일랜드 시민의회(Irish Citizens’ Assembly)는 학계-시민사회 차원의 파일럿 프로젝트로 출발한 이후 2012년부터 2023년까지 6차에 걸쳐 정부와 의회 차원에서 공식적으로 운영되어, 아일랜드 정치과정 내에 준상설화된 숙의민주주의적 혁신 기제로 자리 잡았다. 아일랜드 시민의회는 시민대표와 정당대표의 공동 숙의 가능성, 1년 이상의 밀도 있는 숙의 과정, 국민투표와의 결합으로 동성결혼 합법화와 낙태 허용 등 첨예한 사회문화적 의제에 대한 헌법 개정 성공 등 괄목할 만한 특징과 성취를 보였다. 이 논문은 아일랜드 시민의회의 역사적·정치적 맥락, 제도 설계, 실행 과정, 결과와 효과성 등 여러 측면을 체계적으로 소개 및 토론한다. 이 과정에서 표준적 대의민주주의 보완 기제의 측면과 시민 주도의 대안적 혁신 기제 사이에서 아일랜드 시민의회가 어떤 특징을 보였는지, 그리고 숙의민주주의 제도로서 아일랜드 시민의회가 실제로 어떤 특징과 잠재력을 보였는지를 규명한다.

  PublisherDOI

Frequent coauthors

• Sungjoon Choi

  20 shared

• Katsu Yamane

  17 shared

• Wonmo Sung

  Anyang University

  15 shared

• Sankalp Yamsani

  University of Illinois Urbana-Champaign

  10 shared

• Alexander Alspach

  Walt Disney (United States)

  10 shared

• Kazuki Shin

  University of Illinois Urbana-Champaign

  9 shared

• Matthew Pan

  9 shared

• Kevin G. Gim

  University of Illinois Urbana-Champaign

  8 shared

Labs

• KIMLABPI

Education

• Ph.D., Electrical Engineering and Computer Science

  Seoul National University

  2012

• B.S., Electrical Engineering and Computer Science

  Seoul National University

  2001

Awards & honors

• IEEE-RAS International Conference on Humanoid Robots (Humano…
• IEEE/RSJ International Conference of Intelligent Robots and…
• IEEE International Conference of Robotics and Automation (IC…
• IEEE International Symposium on Robot and Human Interactive…