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KAIST and NVIDIA Launch Human Physical AI NVAITC
A new era of Physical AI is taking shape, enabling wearable robots and humanoids to understand and predict human movement and achieve more precise control. KAIST, which possesses world-class research capabilities in wearable robotics, and NVIDIA will collaborate to develop a Human Motion Foundation Model that enables AI to learn human movement and physical intelligence.
KAIST, led by President Choongsik Bae, announced on July 25 that it will establish a NVIDIA AI Technology Center (NVAITC) with NVIDIA to advance collaborative research in Physical AI.
As the Korean government advances Physical AI as a key national initiative for the country’s future, the collaboration aims to secure core technologies for next-generation Physical AI by combining KAIST’s human-centered robotics technologies and real-world human motion data with NVIDIA AI technologies and global research network.
The collaboration will be carried out through the establishment of the Human Physical AI NVAITC by the KAIST Department of Mechanical Engineering and NVIDIA. The Human Physical AI Research Center at the KAIST Department of Mechanical Engineering will serve as the core research hub for the NVAITC . Building on this foundation, the two organizations plan to progressively expand the scope of their collaboration across the full spectrum of Physical AI, including wearable robots, humanoids, digital twins, and manufacturing.
“Competitiveness in the era of Physical AI will depend not simply on AI itself, but on domain-specific technologies and data grounded in a deep understanding of humans and robots,” said KAIST President Choongsik Bae. “By combining KAIST’s accumulated expertise in human-centered research with NVIDIA’s world-leading AI infrastructure and physical AI technologies, we will realize Physical AI that better understands and supports people and develop KAIST into a global hub leading Physical AI research and industry beyond Korea.”
The Human Physical AI Research Center was established around the laboratories of Professor Kyoungchul Kong, a leading researcher in wearable robotics, and Professor Jung Kim, a leading researcher in biorobotics. Professors Kim and Kong serve as co-directors of the Center.
The Center conducts research to understand how humans move, exert force, and maintain balance in real-world environments and to reproduce these capabilities through AI and robotics. In particular, the large-scale human motion data and gait and movement control technologies accumulated through wearable robotics research are regarded as a critical foundation for developing human-centered Physical AI.
Co-director Professor Kyoungchul Kong is a world-renowned researcher in wearable robotics who has developed robotic technologies for gait assistance and rehabilitation. Through Angel Robotics, a company he founded, he has also led the commercialization of wearable robotics by translating research outcomes into real-world products and services. Through the NVAITC , Professor Kong will lead the development of the Human Motion Foundation Model based on the human motion data and robotic control technologies accumulated by his research team.
On NVIDIA’s side, Charles Cheung, Senior Manager at the NVIDIA AI Technology Center (NVAITC), will participate by providing expert technical consultation and developer support. The NVAITC will also operate research and educational programs using NVIDIA Omniverse and digital twin platforms.
“The KAIST Human Physical AI Research Center has world-class human motion data and research capabilities in wearable robotics,” said Charles Cheung. “This research, which seeks to reproduce human movement through AI, is expected to open new possibilities for Physical AI.”
To ensure the systematic operation of the collaborative research, the two organizations will establish a Steering Committee and review research goals and progress every six months. They also plan to hold an annual international symposium that will bring together researchers from Korea and abroad to share the latest research outcomes and industry trends in Physical AI.
A Student Ambassador Program will also be offered to KAIST students. Through the program, NVIDIA experts will provide lectures and regular office hours and carry out projects with participating students.
The first cohort is expected to consist of five to 10 students. Participants will receive training focused on NVIDIA Omniverse and digital twin technologies and will be awarded certificates upon completion of the program.
The primary objective of the first phase of the collaborative research is to develop a Human Motion Foundation Model.
The Human Motion Foundation Model is a generative AI-based model trained on large-scale human motion data to understand, predict, and generate a wide range of human movements. It is expected to serve as a core enabling technology that will allow wearable robots and humanoids to more accurately identify users’ intentions and movements and respond more naturally.
The technologies developed through the NVAITC are expected to be applied not only to wearable robots that support the rehabilitation and daily lives of people with gait impairments, but also to humanoids that work alongside humans, human movement assessment, and digital healthcare.
Ultimately, the researchers aim to explain from an AI perspective how humans plan movement and control their muscles and joints. Based on this understanding, they seek to create next-generation robotic systems that help people overcome gait impairments and expand human physical capabilities.
The collaboration is also significant because its impact is expected to extend beyond an individual research project and contribute to the broader Physical AI industrial ecosystem in Korea.
Co-directors Professors Jung Kim and Kyoungchul Kong are currently leading in a Deep Tech Scale-up Valley project in the field of Physical AI. By combining Angel Robotics’ experience in technology commercialization, KAIST’s capabilities in robotics, mechanical engineering, and AI, and NVIDIA’s AI technologies and global professional network, the collaboration is expected to support a broad range of activities spanning research and development, talent cultivation, startup support, and technology commercialization.
“This collaboration will provide an important opportunity to take AI research in the Department of Mechanical Engineering to the next level,” said Professor Hyung-Soon Park, Head of the KAIST Department of Mechanical Engineering. “Centered around the Human Physical AI Research Center, we will expand Physical AI research into nationally strategic industries, including robotics and manufacturing.”
2026.07.26 View 1780 -
KAIST Makes Buttons Rise with Light
No wires. No actuators. Shine light on the metal surface, and it rises like a button. KAIST researchers have developed a metal structure that changes shape using light, without any light-absorbing coating. This technology could open new possibilities for tactile interfaces with physical pop-up buttons, shape displays, next-generation wearable devices, and soft robots.
KAIST (President Choongsik Bae) announced on the 20th of July that a research team led by Professor Il-Kwon Oh from the Department of Mechanical Engineering has developed a technology that transforms a flat NiTi shape-memory alloy (SMA) sheet into a “photothermally driven meta-morphing structure” that rises from a flat surface into a three-dimensional form when exposed to light, using only a single UV-laser process.
Next-generation wearable devices and soft robots require technologies that are thin and lightweight while also being capable of changing into desired shapes when needed. Such technologies are attracting attention as a foundation for shape displays, adaptive surfaces, wearable interfaces, and soft robots.
The research team designed precise cutting and folding patterns in a flat metal sheet so that it would transform into a predetermined three-dimensional shape. The design principle is based on kirigami, the art of creating three-dimensional structures by cutting paper.
Shape-memory alloys are special metals that return to a pre-programmed shape when heated to a specific temperature, even after being deformed. Because they are lightweight and can generate large forces, they are widely used as key materials for soft robots and wearable actuators. However, conventional photothermal shape-memory alloy actuators have faced a limitation: nickel-titanium alloy (NiTi) surfaces do not absorb near-infrared light efficiently. To compensate for this, separate light-absorbing coatings such as graphene oxide, polymer composites, or titanium nitride (TiN) thin films have typically been applied.
These external coatings can peel off during repeated operation and require additional processing. They can also increase heat capacity, which may slow the response, creating limitations in both manufacturability and actuation performance.
To address this problem, the research team used UV laser micromachining. Through this process, they formed kirigami structures on thin shape-memory alloy (SMA) sheets while simultaneously generating a micro-nano porous titanium oxide (TiOₓ) layer on the surface through laser-induced oxidation. As a result, they were able to significantly increase the absorption of near-infrared light without any separate external coating.
The team also implemented a platform that can precisely control the height of three-dimensional deformation and the resulting force output by adjusting structural parameters such as hinge width and slit width. In other words, the core of this research lies in simultaneously programming both how the structure mechanically deforms and how efficiently it absorbs light within a single metal structure.
Furthermore, the team applied a patterning technique that spatially controls the degree of laser-induced oxidation. This made it possible for different regions to deform sequentially at different speeds, even when exposed uniformly to light of the same intensity. The researchers describe this as “spatiotemporal actuation control.” This means that the order and timing of deformation are encoded directly into the material itself through light-absorption properties, without any separate electrical control. It can be seen as a form of photonic logic.
The research team further expanded the photothermal SMA metastructure into three-dimensional shape displays and haptic interfaces by integrating it with a multi-channel near-infrared (NIR) LED array. Each SMA kirigami unit moves independently in response to selectively applied light. Based on this, the team successfully displayed the letter sequence K→A→I→S→T and implemented tactile navigation signals that indicate direction.
Professor Il-Kwon Oh said, “The laser programming technology developed in this study is a manufacturing-friendly platform that encodes both mechanical deformation and optical properties into a single metallic structure without any separate coating process,” adding, “It can be widely applied to next-generation intelligent morphing interfaces controlled by light, including adaptive surfaces, interactive haptics, and photothermal soft robots.”
Hyunsoo Kim, a master’s student in the Department of Mechanical Engineering, served as the first author, while Professor Il-Kwon Oh was the corresponding author. The results were published in the international journal Advanced Science, and the work was also selected for the Inside Back Cover of Advanced Science, Vol. 13, No. 31, published on June 4, 2026.
Paper title: Monolithic UV-Laser Programming of Photothermally Meta-Morphing SMA Structures: Dual-Encoded Kirigami Mechanics and Photonic Absorbance
DOI: https://doi.org/10.1002/advs.74930
Related Video: https://drive.google.com/drive/folders/16Q9C1D6EMjoM2ypNmtDGU9ruMBdZeUFs
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00345241 and RS-2023-00302525). This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2025-25441263). This research was supported by the InnoCORE program of the Ministry of Science and ICT (N10250154).
2026.07.20 View 867 -
KAIST Develops Robot That Judges Its Surroundings and Walks, Runs, and Jumps Like an Animal
An era in which robots decide "how to walk" on their own has arrived. A four-legged robot has been developed that, much like a person or an animal, autonomously chooses the appropriate gait strategy for its surroundings — changing its gait on stairs, leaping over gaps, and keeping its balance on forest trails.
KAIST (President Choongsik Bae) announced on the 16th of July that a research team led by Professor Hae-Won Park from the Department of Mechanical Engineering has developed a core control technology for four-legged robots that lets a single controller select and switch in real time among walking, running, jumping, and other locomotion skills, allowing the robot to move quickly and stably, even in real outdoor environments.
Four-legged robots move on four legs, giving them an advantage over wheeled robots on rough terrain. But in real outdoor settings, obstacles such as stairs, ledges, stepping stones, gaps, and tree branches appear one after another in different forms, meaning the ability to simply walk and run fast is not enough.
Existing four-legged robots have excelled at running quickly across flat ground or clearing simple obstacles, but they have struggled to maintain both speed and stability in real-world environments where obstacles combine in complex ways. Because walking, running, jumping, and other gaits had to be controlled individually, the robots were also limited in how naturally they could switch between them as conditions changed.
To overcome these limitations, the research team developed a new learning-based control technology called APT-RL (Action Pretrained Transformer-based Reinforcement Learning).
APT-RL is a control technology designed to enable a robot to first learn a range of locomotion skills — such as walking, running, and jumping — and then freely combine and transition among them in real-world environments as the situation demands.
Rather than filming the movements of real people or animals, the team generated 15.5 hours of training data covering a variety of gaits using computer simulations alone, in just eight minutes. That data was used to teach the robot basic movement capabilities, drawing on robot dynamics (a mathematical model of how a robot moves) and trajectory optimization (a technique for calculating the efficient path of movement). The approach is far faster and more efficient than earlier methods that relied on motion capture, a technology that records human or animal movement using sensors.
The team then applied reinforcement learning — an artificial intelligence technique in which an agent learns optimal behavior through repeated trial and error — so the robot could autonomously select and switch gaits suited to complex three-dimensional terrain such as stairs, ledges, and gaps. Finally, the team combined a depth camera (which measures the distance to objects in order to obtain three-dimensional information) with LiDAR (Laser Detection and Ranging, a sensor that uses lasers to measure the distance and shape of the surrounding environment in three dimensions), enabling the robot to recognize its surroundings and target speed in real time and choose the most appropriate walking strategy.
The team tested the control technology on its own four-legged robot, 'KAIST HOUND.' The experiments were conducted not only on an indoor obstacle course but also in real outdoor environments, including KAIST’s campus and forest trails.
KAIST HOUND moved stably across urban terrain that included stairs, grass, and slopes, as well as irregular natural terrain such as fallen trees, exposed roots, and paths covered in fallen leaves, switching gaits in real time to match the conditions. In rugged terrain with obstacles, the robot reached a peak instantaneous speed of six meters per second (about 22 kilometers per hour), demonstrating that it can achieve both fast movement and stability in real outdoor environments.
The experiments showed that KAIST HOUND autonomously selected and switched between a trot (alternating diagonal legs) and a bound (a leaping gait using the front and back leg pairs together) depending on the terrain and target speed, and that it could integrate walking, running, jumping, and ledge-clearing into a single controller.
Professor Hae-Won Park said "We expect this to become a foundational technology that expands the potential uses of physical-AI-based walking robots in rugged environments such as disaster sites, defense missions, and industrial facility inspections."
Jun-Gill Kang (affiliated with the Agency for Defense Development (ADD) at the time of the research) and Jaehyun Park, a Ph.D. candidate in KAIST's Department of Mechanical Engineering, are co-first authors of the study. Professor Hae-Won Park and Professor Seungwoo Hong from Korea University are co-corresponding authors. The research was selected as the cover paper for the July issue of Science Robotics, the world's leading academic journal in robotics, and was published on July 15 (U.S. Eastern time).
Paper title: Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
DOI: 10.1126/scirobotics.adz7397
Authors: Jun-Gill Kang (the Agency for Defense Development at the time of the research, co-first author), Jaehyun Park (KAIST, co-first author), Hae-Won Park (KAIST, corresponding author), Seungwoo Hong (Korea University, corresponding author)
This research was supported by funding from the Ministry of Trade, Industry and Resources (MOTIR) and the Korea Planning & Evaluation of Industrial Technology (KEIT) (RS-2024-00427719), as well as by the Agency for Defense Development's Future Challenge Defense Technology R&D program (912768601).
2026.07.16 View 1370 -
KAIST Begins Developing the World’s First Brain-to-Robot Technology, Moving Robots by Thought and Sending Sensation Back to the Brain
KAIST researchers have begun developing a next-generation brain-robot interface platform that uses human brain signals to control an exoskeleton in real time and sends the tactile and force information sensed by the robot back to the brain.
KAIST, led by President Kwang-Hyung Lee, announced on the 25th that research teams led by Professors Kyoungchul Kong and Jung Kim of its Department of Mechanical Engineering, together with Angel Robotics Co., Ltd., have launched the world’s first bidirectional “Brain-to-Robot” system as a flagship initiative of the Korea Medical Device Development Fund (KMDF). The project runs from April 2026 to December 2032.
Professor Kyoungchul Kong is a world-renowned wearable-robotics researcher who founded Angel Robotics, a developer of walking-assist exoskeletons, and led his team to back-to-back gold medals at Cybathlon, the international competition for assistive technologies for people with disabilities. Professor Jung Kim is a globally recognized researcher who received the Scientist and Engineer of the Month Award for his work on robotic skin. Together, the two teams have formed a consortium to develop a Brain-to-Robot platform that merges neural interfaces with exoskeleton robotics.
Brain interface technologies that let users move a cursor or operate a smartphone with brain signals have already reached the stage of human clinical trials, and U.S. companies such as Neuralink and Synchron are accelerating their development. Existing approaches, however, have struggled to link actual movement and sensory feedback at the same time. They have also concentrated largely on advancing signal decoding itself, without clearly defining the target of control, namely what the brain signals actually drive and what kind of sensory information is returned.
Brain-to-Robot is designed to overcome these limitations head-on. It sets the exoskeleton itself as the control target: brain signals read the user’s movement intentions to drive the robot, and at the same time the robot’s sensory readings are delivered back to the brain. These readings include ground reaction force (the force the floor exerts on the foot), joint torque (rotational force at the joints), and tactile information. The aim is a complete bidirectional interface.
According to the research team, no fully bidirectional Brain-to-Robot system that combines exoskeleton control with sensory feedback has yet been reported anywhere in the world, and the project is expected to mark a turning point in brain interface technology.
Within this system, the KAIST teams are responsible for the core technologies. Professor Kong’s team will develop wearable-robot control and AI-based interpretation of movement intention, and will design a somatosensory interface, a system for transmitting bodily sensory information, that delivers the robot’s sensory data accurately to the Brain Chip, the semiconductor that processes brain signals.
Professor Kim’s team will develop robotic skin that senses in place of impaired sensation for people with disabilities, along with AI-based interpretation of somatosensory information.
The teams will also develop AI-based encoding and decoding algorithms that turn brain signals into robot commands and send the robot’s sensory information back to the brain. A key challenge is processing hundreds of channels of cortical signals, the neural signals generated in the cerebral cortex, while stably maintaining an ultra-low-latency closed loop, a control cycle in which signals are exchanged continuously in real time.
Commercialization of the flagship project will be led by Angel Robotics (KOSDAQ: 455900), the company founded by Professor Kong. The team plans to pursue full-cycle commercialization, from regulatory approval by the Ministry of Food and Drug Safety through to real-world deployment.
“If this technology succeeds, it will open a new rehabilitation paradigm in which people with quadriplegia can move beyond the hospital to walk on their own, pick up objects, and even feel sensation at their fingertips in everyday life,” Professor Kong said.
The research team stressed that, because this is an unprecedented and highly complex convergence technology never attempted at home or abroad, long-term safety, clinical validation, and a regulatory approval framework must advance in parallel with the technology itself. To reach the global market, they added, safety and efficacy testing, the accumulation of clinical evidence, a risk-management system, protection of brain-signal data, cybersecurity, and ethical review must all be addressed in an integrated way.
Meanwhile, KAIST is conducting a wide range of fundamental research in the field of brain interfaces. A research team led by Professor Hyung-Soon Park of the Department of Mechanical Engineering is studying wearable rehabilitation robot technologies based on neural intention-recognition interfaces, which identify users’ movement intentions from brain signals, for the effective treatment of neurological disorders. A research team led by Professor Sungho Cho of the School of Computing is developing AI-based brain-signal interpretation technologies.
A research team led by Professor Jihoon Lee of the Department of Brain and Cognitive Sciences is conducting next-generation brain–machine interface research focused on ultra-low-power bio/neural interface circuits, which connect and process biological and neural signals with low power consumption; wireless neural signal measurement technologies, which measure neural signals without wires; and on-device AI-based closed-loop neuromodulation technologies, which use cyclical control structures to exchange signals in real time.
In addition, a research team led by Professor Hyunjoo Lee of the School of Electrical Engineering is conducting research on high-resolution neural signal measurement and precision brain stimulation based on ultra-miniaturized multimodal neural electrodes, which can simultaneously measure and stimulate multiple types of neural signals. A research team led by Professor Minkyu Je of the Department of AI Semiconductor Systems is studying AI-based semiconductor integrated circuits and system technologies for next-generation neural interfaces. A research team led by Professor Jae-Woong Jeong of the School of Electrical Engineering is conducting research on high-precision brain-signal measurement, which precisely measures neural signals generated in the brain, and neuroengineering based on neural stimulation.
“This Brain-to-Robot flagship project is a world-class, highly challenging convergence research initiative led by the teams of Professors Kyoungchul Kong and Jung Kim,” said KAIST President Kwang-Hyung Lee. “KAIST has a wide range of researchers studying fundamental technologies in brain interfaces, AI, semiconductors, and robotics, and based on this foundation, we will lead innovation in next-generation Brain-to-Robot technologies.”
2026.06.25 View 2062 -
KAIST Develops Next-Generation Self-Powered Wearable Sensor Resilient to 668% Elongation
Wearable medical devices that monitor heart rate, respiration, and joint movements for long periods without battery concerns, electronic skins that sense external stimuli like human skin, and soft robots made of flexible materials that move freely have all come one step closer to reality. KAIST researchers have developed a self-powered sensor (a sensor that generates electricity on its own without a battery) that can stretch up to 668% while producing stable electrical signals.
KAIST announced on June 18th that a research team led by Professor Miso Kim from the Department of Mechanical Engineering has overcome the durability limitations of conventional piezoelectric fiber sensors (fiber-type sensors that convert pressure or movement into electrical signals) and successfully developed a highly stretchable piezoelectric fiber sensor that operates stably even under repeated deformation.
The core material of the sensor, piezoelectric polymer, is a polymeric material that generates electricity when subjected to mechanical force. Although its lightweight and flexible nature makes it suitable for skin-attachable wearable sensors, conventional piezoelectric fiber sensors suffered from signal degradation during repeated stretching or bending, as the electrode layer collecting electrical signals and the piezoelectric layer generating electricity would become damaged. Furthermore, while coiling the fibers can increase stretchability to allow greater elongation, maintaining electrical stability remained a significant challenge.
To resolve these issues, the research team developed a "Hierarchical Resilient Design" strategy, engineering the sensor to withstand deformation across multiple levels—from its constituent materials and electrodes to its overall structure. Simply put, just as a rubber band returns to its original shape after repeated stretching, the sensor is designed to self-maintain its performance after cyclical deformation.
First, the research team embedded elastic polymer microparticles inside the piezoelectric nanofibers to create a closely interlocking structure. This creates a supportive effect similar to Velcro, helping the sensor recover its original shape even after being repeatedly stretched.
Additionally, they designed the interface so that the electricity-collecting electrode and the electricity-generating piezoelectric layer connect seamlessly. By strongly bonding different materials together, they ensured they would not easily delaminate under impact or deformation, allowing the sensor to maintain a stable electrical signal even when significantly stretched or bent.
Applying this design to a coil structure, the research team successfully stretched the sensor up to 668%—approximately 6.7 times its original length—while maintaining a stable output. The developed sensor generated consistent electrical signals under various movements, including stretching, bending, and pressing.
Furthermore, the research team fabricated the sensor not only in coil forms but also in knot configurations, confirming its stable operation under repeated forces or sudden impacts. By leveraging artificial intelligence (AI) to analyze the sensor signals, they were also able to accurately distinguish between different movements, such as pressing, bending, and stretching.
This study holds great significance as it presents a self-powered sensor platform that simultaneously achieves high stretchability and long-term stability without requiring a battery. In particular, because it enables stable signal measurement in environments undergoing repeated deformation, it is expected to be utilized in developing next-generation wearable medical devices for long-term monitoring of various biosignals, including heart rate, respiration, joint movement, and muscle activity. It is also projected to expand its range of applications to digital healthcare devices, electronic skins, and sensory sensors for soft robots by making devices lighter and more convenient to use.
"The core achievement of this research is that it simultaneously secured mechanical resilience and electrical reliability by combining fiber structure design with electrode interface engineering (a technology that controls the boundary where different materials meet)," said Professor Miso Kim. She added, "In the future, we expect it to be applied to wearable medical devices requiring long-term wear, electronic skins, and sensory sensors for soft robots, enabling more accurate and continuous biosignal monitoring."
The research findings, with researcher Yong Jun Choi as the first author, were published on March 10, 2026, in ACS Nano (Impact Factor 16.1), a world-renowned academic journal in the fields of nanotechnology and materials science.
Paper Title: Mechanically and Functionally Resilient Piezoelectric Fiber Coils and Knots for Reliable Self-Powered Sensing DOI: doi/10.1021/acsnano.5c19628 Author Information: Yong Jun Choi 1 (KAIST, First Author), JungHun Park 1 (KAIST, Co-author), Jisoo Nam 1 (KAIST, Co-author), Gi-Dong Sim 1 (KAIST, Co-author), Myung-Gil Kim 2 (Sungkyunkwan University, Co-author), Miso Kim (KAIST, Corresponding Author)
This research was conducted with support from the BRIDGE Convergence Research and Development Program (RS-2023-00254689), the Nano·Material Technology Development Program (RS-2024-00468995), and the Next-Generation Semiconductor-Compatible Micro-Substrate Technology Development Program (RS-2024-00433654) funded by the National Research Foundation of Korea under the Ministry of Science and ICT.
2026.06.18 View 2984 -
Professor Kuk-Jin Yoon’s Research Team at the Department of Mechanical Engineering Achieves Landmark Success with 10 Papers Accepted at CVPR 2026
<Professor Kuk-Jin Joon from Department of Mechanical Engineering>
Professor Kuk-Jin Yoon’s research team from our university’s Department of Mechanical Engineering has once again demonstrated its overwhelming academic prowess by having a total of 10 papers accepted as lead authors at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026).
CVPR is the most influential international conference in the fields of artificial intelligence and visual intelligence. Since its inception in 1983, it has selected outstanding research through a rigorous peer-review process every year. For CVPR 2026, a total of 16,092 papers were submitted worldwide, with 4,090 accepted, resulting in a competitive acceptance rate of approximately 25.42%. Achieving 10 accepted papers as lead or corresponding authors from a single laboratory is regarded as an exceptionally rare and world-class feat.
Professor Kuk-Jin Yoon’s team conducts extensive research with the ultimate goal of achieving human-level visual intelligence. The papers accepted this year cover cutting-edge topics in computer vision, including:
Event camera-based technologies
Perception technologies for autonomous driving
AI optimization and adaptation techniques
This achievement follows the team's remarkable success at ICCV 2025 last year, where they published 12 papers as lead/corresponding authors. The results at CVPR 2026 further solidify the laboratory's position as a global hub for pioneering computer vision research. The research team plans to continue contributing to the advancement of future AI technologies by tackling challenging research that transcends the limitations of existing methods.
Meanwhile, CVPR 2026 is scheduled to be held in Denver, Colorado, USA, from June 3 to June 7.
<CVPR 2026 (Denver, USA)>
2026.03.06 View 4634 -
KAIST Launches Deep-Tech Scale-up Valley, Unveils Execution Strategies for Physical AI
< Progress Report Meeting of the Deep-Tech Scale-up Valley Project >
KAIST announced on February 27th that it held the "Deep-Tech Scale-up Valley Project Progress Report Meeting" at its main campus in Daejeon on the 26th. During the meeting, the university unveiled its Physical AI strategies and execution structures, currently being developed with a focus on robotics.
The Deep-Tech Scale-up Valley Promotion Project is a joint initiative by the Ministry of Science and ICT, Daejeon Metropolitan City, and KAIST. KAIST has secured a total budget of 13.65 billion KRW for a period of three years and six months, starting from 2025. The project aims to commercialize KAIST's deep-tech capabilities in robotics to build a robust robot innovation ecosystem. A "Robot Alliance" has been formed, led by KAIST (headed by Professor Jung Kim) and including KAIST Holdings, Daejeon Techno Park, Daejeon Center for Creative Economy & Innovation, Angel Robotics, and Eurobotics.
The project seeks to foster a virtuous cycle ecosystem and nurture future "Unicorn" companies based on a three-pillar framework: Technology Commercialization, Deep-Tech R&D, and Commercialization Scale-up. In its first year (2025), the project achieved 230 billion KRW in technology transfers and investment attraction through Physical AI lectures, startup pitching sessions, and investment networking.
Physical AI refers to technology that combines robotics with artificial intelligence, allowing machines to make autonomous decisions and act in the real world. While it is gaining traction as a core field of next-generation industry—with increasing government R&D, corporate investment, and startup activity—critics have noted that successful business models applicable to actual industrial sites remain limited.
This report meeting is significant in that it redefined Physical AI not merely as a competition of AI technology, but as a matter of "industrial structure." It emphasized that commercialization is difficult unless R&D, industrial sites, and the investment ecosystem are organically linked.
Specifically, the report stated that for Physical AI to be applied to industrial sites, "meaningful data" generated from real-world operations is required, going beyond virtual environments. The strategy involves collaborating with skilled experts in manufacturing processes to accumulate data reflecting physical sensations and judgment, and establishing an execution system where robots can continuously cooperate with humans without obstructing their tasks.
Professor Kyoungchul Kong of the KAIST Department of Mechanical Engineering stated, "It is now crucial to clarify the mixed concepts of Physical AI and create a concrete platform that anyone can utilize." He added, "For AI learned in virtual environments to function properly with actual robots in the real world, we must not only improve the accuracy of virtual technologies but also ensure that physical variables in the real world are predictable and stably managed." In simpler terms, technology is needed to ensure that a robot's performance in a simulation translates seamlessly to the real world.
Professor Hyun Myung of the KAIST School of Electrical Engineering highlighted, "In the field of AI, research on Physics-Informed Neural Networks (PINN), which incorporate physical laws into the learning process, is actively underway." He emphasized, "The completion of Physical AI is possible only when hardware researchers, who understand actual physical systems, and AI researchers, who implement these into learning structures, are organically integrated. We need AI that understands physical principles, going beyond simply learning massive amounts of data."
Based on this execution structure, KAIST plans to establish a clear Value Chain connecting researchers, industrial experts, and corporations. The strategy is to expand Physical AI from lab-scale demonstrations to technologies that solve real-world industrial problems.
Jung Kim, Head of the KAIST Department of Mechanical Engineering, stated, "We have moved past the era of competing on data volume; now is the time to contemplate how to execute AI in the physical world. Based on KAIST's specific preparations and execution strategies, we will support startups and companies to succeed in the commercialization of Physical AI."
Meanwhile, the Deep-Tech Scale-up Valley Project plans to step-by-step promote the establishment of a Physical AI platform, startup discovery and investment expansion, the creation of verification testbeds, and the expansion of cooperation networks with global robotics companies.
2026.02.27 View 3386 -
Direct Printing of Nanolasers, the Key to Optical Computing and Quantum Security
< (From left) Professor Ji Tae Kim (KAIST), Dr. Shiqi Hu (First Author, AI-based Intelligent Design-Manufacturing Integrated Research Group, KAIST-POSTECH), and Professor Junsuk Rho (POSTECH) >
In future high-tech industries, such as high-speed optical computing for massive AI, quantum cryptographic communication, and ultra-high-resolution augmented reality (AR) displays, nanolasers—which process information using light—are gaining significant attention as core components for next-generation semiconductors. A research team at our university has proposed a new manufacturing technology capable of high-density placement of nanolasers on semiconductor chips, which process information in spaces thinner than a human hair.
KAIST announced on January 6th that a joint research team, led by Professor Ji Tae Kim from the Department of Mechanical Engineering and Professor Junsuk Rho from POSTECH (President Seong-keun Kim), has developed an ultra-fine 3D printing technology capable of creating "vertical nanolasers," a key component for ultra-high-density optical integrated circuits.
Conventional semiconductor manufacturing methods, such as lithography, are effective for mass-producing identical structures but face limitations: the processes are complex and costly, making it difficult to freely change the shape or position of devices. Furthermore, most existing lasers are built as horizontal structures lying flat on a substrate, which consumes significant space and suffers from reduced efficiency due to light leakage into the substrate.
To solve these issues, the research team developed a new 3D printing method to vertically stack perovskite, a next-generation semiconductor material that generates light efficiently. This technology, known as "ultra-fine electrohydrodynamic 3D printing," uses electrical voltage to precisely control invisible ink droplets at the attoliter scale ($10^{-18}$ L).
Through this method, the team successfully printed pillar-shaped nanostructures—much thinner than a human hair—directly and vertically at desired locations without the need for complex subtractive processes (carving material away).
The core of this technology lies in significantly increasing laser efficiency by making the surface of the printed perovskite nanostructures extremely smooth. By combining the printing process with gas-phase crystallization control technology, the team achieved high-quality structures with nearly single-crystalline alignment. As a result, they were able to realize high-efficiency vertical nanolasers that operate stably with minimal light loss.
Additionally, the team demonstrated that the color of the emitted laser light could be precisely tuned by adjusting the height of the nanostructures. Utilizing this, they created laser security patterns invisible to the naked eye—identifiable only with specialized equipment—confirming the potential for commercialization in anti-counterfeiting technology.
< 3D Printing of Perovskite Nanolasers >
Professor Jitae Kim stated, "This technology allows for the direct, high-density implementation of optical computing semiconductors on a chip without complex processing. It will accelerate the commercialization of ultra-high-speed optical computing and next-generation security technologies."
The research results, with Dr. Shiqi Hu from the Department of Mechanical Engineering as the first author, were published online on December 6, 2025, in ACS Nano, an international prestigious journal in the field of nanoscience.
Paper Title: Nanoprinting with Crystal Engineering for Perovskite Lasers
DOI: https://doi.org/10.1021/acsnano.5c16906
This research was conducted with support from the Ministry of Science and ICT’s Excellent Young Researcher Program (RS-2025-00556379), the Mid-career Researcher Support Program (RS-2024-00356928), and the InnoCORE AI-based Intelligent Design-Manufacturing Integrated Research Group (N10250154).
2026.01.06 View 3651 -
KAIST Develops Room-Temperature 3D Printing Technology for ‘Electronic Eyes’—Miniaturized Infrared Sensors
<(From Left) Professor Ji Tae Kim of the Department of Mechanical Engineering, Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong>
The “electronic eyes” technology that can recognize objects even in darkness has taken a step forward. Infrared sensors, which act as the “seeing” component in devices such as LiDAR for autonomous vehicles, 3D face recognition systems in smartphones, and wearable healthcare devices, are regarded as key components in next-generation electronics. Now, a research team at KAIST and their collaborators have developed the world’s first room-temperature 3D printing technology that can fabricate miniature infrared sensors in any desired shape and size.
KAIST (President Kwang Hyung Lee) announced on the 3rd of November that the research team led by Professor Ji Tae Kim of the Department of Mechanical Engineering, in collaboration with Professor Soong Ju Oh of Korea University and Professor Tianshuo Zhao of the University of Hong Kong, has developed a 3D printing technique capable of fabricating ultra-small infrared sensors—smaller than 10 micrometers (µm)—in customized shapes and sizes at room temperature.
Infrared sensors convert invisible infrared signals into electrical signals and serve as essential components in realizing future electronic technologies such as robotic vision. Accordingly, miniaturization, weight reduction, and flexible form-factor design have become increasingly important.
Conventional semiconductor fabrication processes were well suited for mass production but struggled to adapt flexibly to rapidly changing technological demands. They also required high-temperature processing, which limited material choices and consumed large amounts of energy.
To overcome these challenges, the research team developed an ultra-precise 3D printing process that uses metal, semiconductor, and insulator materials in the form of liquid nanocrystal inks, stacking them layer by layer within a single printing platform.
This method enables direct fabrication of core components of infrared sensors at room temperature, allowing for the realization of customized miniature sensors of various shapes and sizes.
Particularly, the researchers achieved excellent electrical performance without the need for high-temperature annealing by applying a “ligand-exchange” process, where insulating molecules on the surface of nanoparticles are replaced with conductive ones.
As a result, the team successfully fabricated ultra-small infrared sensors measuring less than one-tenth the thickness of a human hair (under 10 µm).
<Figure 1. 3D printing of infrared sensors.a. Room-temperature printing process for the electrodes and photoactive layer that make up the infrared sensor.b. Structure and chemical composition of the printed infrared microsensor. c.Printed infrared sensor micropixel array.>
Professor Ji Tae Kim commented, “The developed 3D printing technology not only advances the miniaturization and lightweight design of infrared sensors but also paves the way for the creation of innovative new form-factor products that were previously unimaginable. Moreover, by reducing the massive energy consumption associated with high-temperature processes, this approach can lower production costs and enable eco-friendly manufacturing—contributing to the sustainable development of the infrared sensor industry.”
The research results were published online in Nature Communications on October 16, 2025, under the title “Ligand-exchange-assisted printing of colloidal nanocrystals to enable all-printed sub-micron optoelectronics” (DOI: https://doi.org/10.1038/s41467-025-64596-4).
This research was supported by the Ministry of Science and ICT of Korea through the Excellent Young Researcher Program (RS−2025−00556379), the National Strategic Technology Material Development Program (RS−2024−00407084), and the International Cooperation Research Program for Original Technology Development (RS−2024−00438059).
2025.11.03 View 6651 -
KAIST Fabricates Green Hydrogen Cells in Just 10 Minutes Like Using a Microwave
<(From Left) Ph.D candidate Hyeongmin Yu, Ph.D candidate Seungsoo Jang, Ph.D candidate Donghun Lee, Ph.D candidate Gayoung Yoon, Professor Kang Taek Lee>
Solid oxide electrolysis cells (SOECs), a key technology for producing green hydrogen without carbon emissions, require a high-temperature “sintering” process to harden ceramic powders. Researchers at KAIST have successfully shortened this process from six hours to just ten minutes, while also reducing the required temperature from 1,400°C to 1,200°C. This innovation dramatically cuts both energy consumption and production time, marking a major step forward for the green hydrogen era.
KAIST (President Kwang Hyung Lee) announced on the 25th of October that a research team led by Professor Kang Taek Lee from the Department of Mechanical Engineering has developed an ultra-fast manufacturing method capable of producing high-performance green hydrogen electrolysis cells in only ten minutes.
The core of this technology lies in sintering—a process in which ceramic powders are baked at high temperatures to form a dense, tightly bonded structure. Proper sintering is critical: it ensures that gases do not leak (as hydrogen and oxygen mixing could cause explosions), oxygen ions move efficiently, and the electrodes adhere firmly to the electrolyte to allow smooth current flow. In short, the precision of the sintering process directly determines the cell’s performance and lifetime.
To address these challenges, the KAIST team applied a “volumetric heating” technique that uses microwaves to heat the material uniformly from the inside out. This approach shortened the sintering process by more than thirtyfold compared to conventional methods. Whereas traditional sintering requires prolonged heating above 1,400°C, the new process uses microwaves to heat the material internally and evenly, achieving stable electrolyte formation at just 1,200°C within 10 minutes.
In conventional fabrication, the essential materials—ceria (CeO₂) and zirconia (ZrO₂)—tend to intermix at excessively high temperatures, degrading material quality. KAIST’s new method allows these two materials to bond firmly at the right temperature without mixing, producing a dense, defect-free bilayer electrolyte.
The total “processing time” includes heating, holding, and cooling. The conventional sintering process required about 36.5 hours, whereas KAIST’s microwave-based technique completes the entire cycle in only 70 minutes—over 30 times faster.
<Figure 1. (a) Schematic illustration of the microwave-based ultrafast sintering process and the conventional sintering process (b) Cross-sectional SEM images of the bilayer ceramic electrolyte according to the sintering process>
The resulting electrochemical cells demonstrated remarkable performance: they produced 23.7 mL of hydrogen per minute at 750°C, maintained stable operation for over 250 hours, and exhibited excellent durability. Using 3D digital twin simulations, the team further revealed that ultra-fast microwave heating improves electrolyte density and suppresses abnormal grain growth of nickel oxide (NiO) particles within the fuel electrode, thereby enhancing hydrogen production efficiency.
<Figure 2. 3D reconstruction, contact area, and electrochemically active site images of the solid oxide electrochemical cell according to the sintering process>
Professor Kang Taek Lee stated, “This research introduces a new manufacturing paradigm that enables the rapid and efficient production of high-performance solid oxide electrolysis cells.” He added, “Compared to conventional processes, our approach drastically reduces both energy consumption and production time, offering strong potential for commercialization.”
This study was co-first-authored by Hyeongmin Yu and Seungsoo Jang, both Ph.D. candidates in Mechanical Engineering at KAIST, with Donghun Lee and Gayoung Youn as collaborators. The research was published online on October 2 in Advanced Materials (Impact Factor: 26.8) and was selected as the Inside Front Cover feature paper for its scientific significance.
※ Paper title: “Ultra-Fast Microwave-Assisted Volumetric Heating Engineered Defect-Free Ceria/Zirconia Bilayer Electrolytes for Solid Oxide Electrochemical Cells”, DOI: 10.1002/adma.202500183)
This work was supported by the Ministry of Science and ICT through the H2 Next Round Program, the Mid-Career Researcher Program, and the Global Research Laboratory (GRL) Program.
2025.10.29 View 3822 -
Ultra High Speed Optical Measurement Technology Developed
< (From left) Tae Jin Ha, CEO of VIRNECT, Kwang Hyung Lee, President of KAIST >
An open platform for industry-academia-research collaboration, which has accumulated K-Metaverse technology capabilities that break down the boundaries between reality and virtuality and share experiences beyond the limits of time and space, is expected to be built on our university campus.
Our university announced on the 13th that it’s signing an agreement for the establishment and operation of a 'Virtual Convergence Research Center' with the Graduate School of Metaverse and VIRNECT Co., Ltd. (CEO Tae Jin Ha), a domestic augmented/virtual reality (XR) specialized company and a startup founded by a KAIST alumnus.
The Virtual Convergence Research Center, which will be newly constructed on our university campus, plans to prepare for the future participation of related government-funded research institutes, and is expected to function as a national strategic hub that creates future growth engines for the Republic of Korea, going beyond simple industry-academia cooperation. VIRNECT Co., Ltd. plans to create the research center as an open research collaboration platform in which domestic and international industry, academia, and research institutes jointly participate with KAIST.
This research center is expected to experiment with the convergence of reality and virtuality and establish itself as a global hub for the 'K-Metaverse Innovation Ecosystem' where technology development, talent cultivation, and industrial diffusion are in a virtuous cycle.
VIRNECT Co., Ltd. was founded by KAIST alumnus Tae Jin Ha, listed on KOSDAQ in 2023, and won the CES Innovation Award for developing the industrial AI smart goggles 'VisionX'. It has grown into a representative domestic spatial computing company based on various industrial innovation technologies such as AI/XR solutions and digital twin. Synergistic co-prosperity with KAIST is anticipated through this collaboration.
Spatial computing and XR technology are areas where global big techs like Apple, Meta, Google, Microsoft, and Samsung are engaged in fierce competition for dominance, paying attention to them as the next-generation AI platforms. With major countries such as the US and China investing enormous capital and capabilities, the launch of the KAIST Virtual Convergence Research Center is evaluated as a strategic response for South Korea not to fall behind in the competition of the post-metaverse era.
The research center plans to lead both industrial productivity and social innovation as an R&BD (Research & Business Development) hub that integrates core technologies such as digital twin, metaverse, spatial/physical intelligence, and wearable XR. Furthermore, it will quickly verify the applicability to industrial sites and support the creation of new industries through a full-cycle system covering education, research, demonstration, commercialization, and diffusion.
Moreover, the research center will create national synergy by being closely linked with government policies. Strengthening the link between education and research, fostering a sustainable metaverse ecosystem, and expanding global leadership through an open industry-academia-research platform align with the government's strategy for advancing the virtual convergence industry.
< Executives from both organizations attending the signing ceremony >
VIRNECT CEO Tae Jin Ha said, "The long-term cooperation with KAIST is a stepping stone for us to leap forward as a game-changer in the global XR industry," adding, "We will strengthen virtual convergence technology competitiveness through research and education infrastructure and accelerate commercialization through demonstration."
Professor Woontack Woo, Dean of the Graduate School of Metaverse, emphasized, "The Virtual Convergence Research Center will serve as an open platform where industry, academia, and research institutes jointly experiment with K-Metaverse innovation, and a 'Meta Power Plant' that cultivates future core personnel and disseminates research results to the industry."
KAIST President Kwang Hyung Lee said, "This agreement is a strategic investment to secure global leadership by breaking down the boundaries between research and industry, going beyond simply creating a new research center," and "KAIST will spare no support for the research center."
With the future designation of the KAIST Virtual Convergence Research Center as a government-specialized graduate school/research center for the virtual convergence industry and increased industry cooperation, it will establish itself as a national innovation platform that concentrates South Korea's metaverse capabilities. This is expected to lead to the creation of new value for the future society and the strengthening of national competitiveness, going beyond simple technology development.
2025.10.13 View 3210 -
3D Printing Becomes Stronger and More Economical with Light and AI
<(Front) Ph.D. candidate Jisoo Nam, (Back row, from left) Ph.D. candidate Boxin Chen, Professor Miso Kim>
Photocurable 3D printing, widely used for everything from dental treatments to complex prototype manufacturing, is fast and precise but has the limitation of being fragile and easily broken by impact. A KAIST research team has developed a new technology to overcome this weakness, paving the way for the more robust and economical production of everything from medical implants to precision machine parts.
KAIST (President Kwang Hyung Lee) announced on the 29th that Professor Miso Kim's research team in the Department of Mechanical Engineering has developed a new technology that fundamentally resolves the durability limitations of photocurable 3D printing.
Digital Light Processing (DLP)-based 3D printing is a technique that uses light to solidify liquid resin (polymer) to rapidly manufacture precise structures, used in various fields such as dentistry and precision machinery. While traditional injection molding offers excellent durability, it requires significant time and cost for mold fabrication. In contrast, photocurable 3D printing allows for flexible shape realization but has a durability drawback.
Professor Kim's team solved this problem by combining two key elements:
A new photocurable resin material that absorbs shock and vibration while allowing for a wide range of properties from rubber to plastic.
A machine learning-based design technology that automatically assigns optimal strength to each part of the structure.
<Figure 1. Schematic of a new manufacturing technology for high-durability photocurable 3D printing using light-controlled gradient structures. This approach integrates the development of stiffness-controllable viscoelastic polyurethane acrylate (PUA) materials, machine learning-based property gradient optimization, and grayscale DLP 3D printing. The technology enhances damping performance and alleviates stress concentration, providing an integrated solution for high reliability, durability, and customized manufacturing. It demonstrates potential applications in structural components subjected to repetitive loads such as joints, automotive interior parts, and precision machinery components>
The research team developed a Polyurethane Acrylate (PUA) material incorporating dynamic bonds, which significantly increases shock and vibration absorption capability compared to existing materials. Furthermore, they successfully applied 'grayscale DLP' technology, which controls the light intensity to achieve different strengths from a single resin composition, thereby assigning customized strength to specific areas within the structure. This concept is inspired by the harmonious and different roles played by bone and cartilage in the human body.
A machine learning algorithm automatically proposes the optimal strength distribution by analyzing the structure and load conditions. This organically connects material development and structural design, enabling customized strength distribution.
The economic efficiency is also noteworthy. Previously, expensive 'multi-material printing' technology was required to achieve diverse material properties, but this new technology yields the same effect with a single material and a single process, significantly reducing production costs. It eliminates the need for complex equipment or material management, and the AI-based structural optimization shortens research and development time and product design costs.
Professor Miso Kim explained, "This technology simultaneously expands the degrees of freedom in material properties and structural design. Patient-specific implants will become more durable and comfortable, and precision machine parts can be manufactured more robustly." She added, "The fact that it secures economic viability by realizing various strengths with a single material and single process is highly significant," and "We anticipate its utilization across various industrial fields such as biomedical, aerospace, and robotics."
The research was spearheaded by Professor Miso Kim's team at the KAIST Department of Mechanical Engineering, with Ph.D. candidate Jisoo Nam as the first author. Boxin Chen, a student from Sungkyunkwan University, also contributed to the collaborative research. The findings were published online on July 16 in the world-renowned journal in materials science, Advanced Materials (IF 26.8). Recognizing the research's excellence, it was also selected for the journal's Frontispiece.
Paper Title: Machine Learning-Driven Grayscale Digital Light Processing for Mechanically Robust 3D-Printed Gradient Materials
DOI: 10.1002/adma.202504075
The achievements of this research have brought Professor Miso Kim significant international attention, as she simultaneously received the 'Wiley Rising Star Award' and the 'Wiley Women in Materials Science Award' in July 2025, hosted by the international academic publisher Wiley.
The Wiley Rising Star Award is given to emerging researchers with the potential for academic leadership, and the Wiley Women in Materials Science Award is a prestigious honor established to celebrate outstanding female scientists in the field of materials science.
<Figure 2. Frontispiece image (scheduled for Issue 42). Multi-property structure fabricated using a photocurable 3D printer. By varying the projector light intensity by location, stronger light creates rigid regions while weaker light forms flexible ones. AI designs an optimized pattern for the structural shape to prevent fracture and reinforce the overall strength.>
This research was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. NRF-2021R1A2C2095767, RS-2023-00254689, and RS-2024-00433654).
2025.09.30 View 5043