Kaist

High-Resolution Spectrometer that Fits into Smartp..

- Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering develops an ultra-compact, high-resolution spectrometer using 'double-layer disordered metasurfaces' that generate unique random patterns depending on light's color.   - Unlike conventional dispersion-based spectrometers that were difficult to apply to portable devices, this new concept spectrometer technology achieves 1nm-level high resolution in a device smaller than 1cm, comparable in size to a fingernail.   - It can be utilized as a built-in spectrometer in smartphones and wearable devices in the future, and can be expanded to advanced optical technologies such as hyperspectral imaging and ultrafast imaging.       < Photo 1. (From left) Professor Mooseok Jang, Dong-gu Lee (Ph.D. candidate), Gookho Song (Ph.D. candidate) >   Color, as the way light's wavelength is perceived by the human eye, goes beyond a simple aesthetic element, containing important scientific information like a substance's composition or state. Spectrometers are optical devices that analyze material properties by decomposing light into its constituent wavelengths, and they are widely used in various scientific and industrial fields, including material analysis, chemical component detection, and life science research. Existing high-resolution spectrometers were large and complex, making them difficult for widespread daily use. However, thanks to the ultra-compact, high-resolution spectrometer developed by KAIST researchers, it is now expected that light's color information can be utilized even within smartphones or wearable devices.   KAIST (President Kwang Hyung Lee) announced on the 13th that Professor Mooseok Jang's research team at the Department of Bio and Brain Engineering has successfully developed a reconstruction-based spectrometer technology using double-layer disordered metasurfaces＊.   ＊Double-layer disordered metasurface: An innovative optical device that complexly scatters light through two layers of disordered nanostructures, creating unique and predictable speckle patterns for each wavelength.   Existing high-resolution spectrometers have a large form factor, on the order of tens of centimeters, and require complex calibration processes to maintain accuracy. This fundamentally stems from the operating principle of traditional dispersive elements, such as gratings and prisms, which separate light wavelengths along the propagation direction, much like a rainbow separates colors. Consequently, despite the potential for light's color information to be widely useful in daily life, spectroscopic technology has been limited to laboratory or industrial manufacturing environments.       < Figure 1. Through a simple structure consisting of a double layer of disordered metasurfaces and an image sensor, it was shown that speckles of predictable spectral channels with high spectral resolution can be generated in a compact form factor. The high similarity between the measured and calculated speckles was used to solve the inverse problem and verify the ability to reconstruct the spectrum. >   The research team devised a method that departs from the conventional spectroscopic paradigm of using diffraction gratings or prisms, which establish a one-to-one correspondence between light's color information and its propagation direction, by utilizing designed disordered structures as optical components. In this process, they employed metasurfaces, which can freely control the light propagation process using structures tens to hundreds of nanometers in size, to accurately implement 'complex random patterns (speckle＊)'.   ＊Speckle: An irregular pattern of light intensity created by the interference of multiple wavefronts of light.   Specifically, they developed a method that involves implementing a double-layer disordered metasurface to generate wavelength-specific speckle patterns and then reconstructing precise color information (wavelength) of the light from the random patterns measured by a camera.   As a result, they successfully developed a new concept spectrometer technology that can accurately measure light across a broad range of visible to infrared (440-1,300nm) with a high resolution of 1 nanometer (nm) in a device smaller than a fingernail (less than 1cm) using only a single image capture.       < Figure 2. A disordered metasurface is a metasurface with irregularly arranged structures ranging from tens to hundreds of nanometers in size. In a double-layer structure, a propagation space is placed between the two metasurfaces to control the output speckle with high degrees of freedom, thereby achieving a spectral resolution of 1 nm even in a form factor smaller than 1 cm. >   Dong-gu Lee, a lead author of this study, stated, "This technology is implemented in a way that is directly integrated with commercial image sensors, and we expect that it will enable easy acquisition and utilization of light's wavelength information in daily life when built into mobile devices in the future."   Professor Mooseok Jang said, "This technology overcomes the limitations of existing RGB three-color based machine vision fields, which only distinguish and recognize three color components (red, green, blue), and has diverse applications. We anticipate various applied research for this technology, which expands the horizon of laboratory-level technology to daily-level machine vision technology for applications such as food component analysis, crop health diagnosis, skin health measurement, environmental pollution detection, and bio/medical diagnostics." He added, "Furthermore, it can be extended to various advanced optical technologies such as hyperspectral imaging, which records wavelength and spatial information simultaneously with high resolution, 3D optical trapping technology, which precisely controls light of multiple wavelengths into desired forms, and ultrafast imaging technology, which captures phenomena occurring in very short periods."   This research was collaboratively led by Dong-gu Lee (Ph.D. candidate) and Gookho Song (Ph.D. candidate) from the KAIST Department of Bio and Brain Engineering as co-first authors, with Professor Mooseok Jang as the corresponding author. The findings were published online in the international journal Science Advances on May 28, 2025.   ＊ Paper Title: Reconstructive spectrometer using double-layer disordered metasurfaces ＊ DOI: 10.1126/sciadv.adv2376   This research was supported by the Samsung Research Funding and Incubation Center of Samsung Electronics grant, the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT).

KAIST Successfully Develops High-Performance Water..

  < Photo 1. (Front row, from left) Jeesoo Park (Ph.D. Candidate), Professor Hee-Tak Kim (Back row, from left) Kyunghwa Seok (Ph.D. Candidate), Dr. Gisu Doo, Euntaek Oh (Ph.D. Candidate) >   Hydrogen is gaining attention as a clean energy source that emits no carbon. Among various methods, water electrolysis, which splits water into hydrogen and oxygen using electricity, is recognized as an eco-friendly hydrogen production method. Specifically, proton exchange membrane water electrolysis (PEMWE) is considered a next-generation hydrogen production technology due to its ability to produce high-purity hydrogen at high pressure. However, existing PEMWE technology has faced limitations in commercialization due to its heavy reliance on expensive precious metal catalysts and coating materials. Korean researchers have now proposed a new solution to address these technical and economic bottlenecks.   KAIST (President Kwang Hyung Lee) announced on June 11th that a research team led by Professor Hee-Tak Kim of the Department of Chemical and Biomolecular Engineering, in a joint study with Dr. Gisu Doo of the Korea Institute of Energy Research (KIER, President Chang-keun Lee), has developed a next-generation water electrolysis technology that achieves high performance without the need for expensive platinum (Pt) coating.   The research team focused on the primary reason why 'iridium oxide (IrOx),' a highly active catalyst for water electrolysis electrodes, fails to perform optimally. They found that this is due to inefficient electron transfer and, for the first time in the world, demonstrated that performance can be maximized simply by controlling the catalyst particle size.   In this study, it was revealed that the reason iridium oxide catalysts do not exhibit excellent performance without platinum coating is due to 'electron transport resistance' that occurs at the interface between the catalyst, the ion conductor (hereinafter referred to as ionomer), and the Ti (titanium) substrate—core components inherently used together in water electrolysis electrodes.   Specifically, they identified that the 'pinch-off' phenomenon, where the electron pathway is blocked between the catalyst, ionomer, and titanium substrate, is the critical cause of reduced conductivity. The ionomer has properties close to an electron insulator, thereby hindering electron flow when it surrounds catalyst particles. Furthermore, when the ionomer comes into contact with the titanium substrate, an electron barrier forms on the surface oxide layer of the titanium substrate, significantly increasing resistance.       < Figure 1. Infographic related to electron transport resistance at the catalyst layer/diffusion layer interface >   To address this, the research team fabricated and compared catalysts of various particle sizes. Through single-cell evaluation and multiphysics simulations, they demonstrated, for the first time globally, that when iridium oxide catalyst particles with a size of 20 nanometers (nm) or larger are used, the ionomer mixed region decreases, ensuring an electron pathway and restoring conductivity.   Moreover, they successfully optimized the interfacial structure through precise design, simultaneously ensuring both reactivity and electron transport. This achievement demonstrated that the previously unavoidable trade-off between catalyst activity and conductivity can be overcome through meticulous interfacial design.   This breakthrough is expected to be a significant milestone not only for the development of high-performance catalyst materials but also for the future commercialization of proton exchange membrane water electrolysis systems that can achieve high efficiency while drastically reducing the amount of precious metals used.   Professor Hee-Tak Kim stated, "This research presents a new interface design strategy that can resolve the interfacial conductivity problem, which was a bottleneck in high-performance water electrolysis technology." He added, "By securing high performance even without expensive materials like platinum, it will be a stepping stone closer to realizing a hydrogen economy."   This research, with Jeesoo Park, a Ph.D. student from the Department of Chemical and Biomolecular Engineering at KAIST, as the first author, was published on June 7th in 'Energy & Environmental Science' (IF: 32.4, 2025), a leading international journal in the energy and environmental fields, and was recognized for its innovativeness and impact. (Paper title: On the interface electron transport problem of highly active IrOx catalysts, DOI: 10.1039/D4EE05816J).   This research was supported by the New and Renewable Energy Core Technology Development Project of the Ministry of Trade, Industry and Energy.

KAIST Succeeds in Real-Time Carbon Dioxide Monitor..

  < (From left) Master's Student Gyurim Jang, Professor Kyeongha Kwon >   KAIST (President Kwang Hyung Lee) announced on June 9th that a research team led by Professor Kyeongha Kwon from the School of Electrical Engineering, in a joint study with Professor Hanjun Ryu's team at Chung-Ang University, has developed a self-powered wireless carbon dioxide (CO2) monitoring system. This innovative system harvests fine vibrational energy from its surroundings to periodically measure CO2 concentrations.   This breakthrough addresses a critical need in environmental monitoring: accurately understanding "how much" CO2 is being emitted to combat climate change and global warming. While CO2 monitoring technology is key to this, existing systems largely rely on batteries or wired power system, imposing limitations on installation and maintenance. The KAIST team tackled this by creating a self-powered wireless system that operates without external power.   The core of this new system is an "Inertia-driven Triboelectric Nanogenerator (TENG)" that converts vibrations (with amplitudes ranging from 20-4000 ㎛ and frequencies from 0-300 Hz) generated by industrial equipment or pipelines into electricity. This enables periodic CO2 concentration measurements and wireless transmission without the need for batteries.       < Figure 1. Concept and configuration of self-powered wireless CO2 monitoring system using fine vibration harvesting (a) System block diagram (b) Photo of fabricated system prototype >   The research team successfully amplified fine vibrations and induced resonance by combining spring-attached 4-stack TENGs. They achieved stable power production of 0.5 mW under conditions of 13 Hz and 0.56 g acceleration. The generated power was then used to operate a CO2 sensor and a Bluetooth Low Energy (BLE) system-on-a-chip (SoC).   Professor Kyeongha Kwon emphasized, "For efficient environmental monitoring, a system that can operate continuously without power limitations is essential." She explained, "In this research, we implemented a self-powered system that can periodically measure and wirelessly transmit CO2 concentrations based on the energy generated from an inertia-driven TENG." She added, "This technology can serve as a foundational technology for future self-powered environmental monitoring platforms integrating various sensors."       < Figure 2. TENG energy harvesting-based wireless CO2 sensing system operation results (c) Experimental setup (d) Measured CO2 concentration results powered by TENG and conventional DC power source >   This research was published on June 1st in the internationally renowned academic journal ＇Nano Energy (IF 16.8)＇. Gyurim Jang, a master's student at KAIST, and Daniel Manaye Tiruneh, a master's student at Chung-Ang University, are the co-first authors of the paper.   ＊Paper Title: Highly compact inertia-driven triboelectric nanogenerator for self-powered wireless CO2 monitoring via fine-vibration harvesting   ＊DOI: 10.1016/j.nanoen.2025.110872   This research was supported by the Saudi Aramco-KAIST CO2 Management Center.

KAIST Professor Jee-Hwan Ryu Receives Global IEEE ..

- Professor Jee-Hwan Ryu of Civil and Environmental Engineering receives the Best Paper Award from the Institute of Electrical and Electronics Engineers (IEEE) Robotics Journal, officially presented at ICRA, a world-renowned robotics conference.   - This is the highest level of international recognition, awarded to only the top 5 papers out of approximately 1,500 published in 2024.   - Securing a new working channel technology for soft growing robots expands the practicality and application possibilities in the field of soft robotics.       < Professor Jee-Hwan Ryu (left), Nam Gyun Kim, Ph.D. Candidate (right) from the KAIST Department of Civil and Environmental Engineering and KAIST Robotics Program >   KAIST (President Kwang-Hyung Lee) announced on the 6th that Professor Jee-Hwan Ryu from the Department of Civil and Environmental Engineering received the 2024 Best Paper Award from the Robotics and Automation Letters (RA-L), a premier journal under the IEEE, at the '2025 IEEE International Conference on Robotics and Automation (ICRA)' held in Atlanta, USA, on May 22nd.   This Best Paper Award is a prestigious honor presented to only the top 5 papers out of approximately 1,500 published in 2024, boasting high international competition and authority.   The award-winning paper by Professor Ryu proposes a novel working channel securing mechanism that significantly expands the practicality and application possibilities of 'Soft Growing Robots,' which are based on soft materials that move or perform tasks through a growing motion similar to plant roots.       < IEEE Robotics Journal Award Ceremony >   Existing soft growing robots move by inflating or contracting their bodies through increasing or decreasing internal pressure, which can lead to blockages in their internal passages. In contrast, the newly developed soft growing robot achieves a growing function while maintaining the internal passage pressure equal to the external atmospheric pressure, thereby successfully securing an internal passage while retaining the robot's flexible and soft characteristics.   This structure allows various materials or tools to be freely delivered through the internal passage (working channel) within the robot and offers the advantage of performing multi-purpose tasks by flexibly replacing equipment according to the working environment.   The research team fabricated a prototype to prove the effectiveness of this technology and verified its performance through various experiments. Specifically, in the slide plate experiment, they confirmed whether materials or equipment could pass through the robot's internal channel without obstruction, and in the pipe pulling experiment, they verified if a long pipe-shaped tool could be pulled through the internal channel.       < Figure 1. Overall hardware structure of the proposed soft growing robot (left) and a cross-sectional view composing the inflatable structure (right) >   Experimental results demonstrated that the internal channel remained stable even while the robot was growing, serving as a key basis for supporting the technology's practicality and scalability.   Professor Jee-Hwan Ryu stated, "This award is very meaningful as it signifies the global recognition of Korea's robotics technology and academic achievements. Especially, it holds great significance in achieving technical progress that can greatly expand the practicality and application fields of soft growing robots. This achievement was possible thanks to the dedication and collaboration of the research team, and I will continue to contribute to the development of robotics technology through innovative research."       < Figure 2. Material supplying mechanism of the Soft Growing Robot >   This research was co-authored by Dongoh Seo, Ph.D. Candidate in Civil and Environmental Engineering, and Nam Gyun Kim, Ph.D. Candidate in Robotics. It was published in IEEE Robotics and Automation Letters on September 1, 2024.   (Paper Title: Inflatable-Structure-Based Working-Channel Securing Mechanism for Soft Growing Robots, DOI: 10.1109/LRA.2024.3426322)   This project was supported simultaneously by the National Research Foundation of Korea's Future Promising Convergence Technology Pioneer Research Project and Mid-career Researcher Project.

KAIST Introduces ‘Virtual Teaching Assistant’ That..

- Research teams led by Prof. Yoonjae Choi (Kim Jaechul Graduate School of AI) and Prof. Hwajeong Hong (Department of Industrial Design) at KAIST developed a Virtual Teaching Assistant (VTA) to support learning and class operations for a course with 477 students.   - The VTA responds 24/7 to students’ questions related to theory and practice by referencing lecture slides, coding assignments, and lecture videos.   - The system’s source code has been released to support future development of personalized learning support systems and their application in educational settings.       < Photo 1. (From left) PhD candidate Sunjun Kweon, Master's candidate Sooyohn Nam, PhD candidate Hyunseung Lim, Professor Hwajung Hong, Professor Yoonjae Choi >   “At first, I didn’t have high expectations for the Virtual Teaching Assistant (VTA), but it turned out to be extremely helpful—especially when I had sudden questions late at night, I could get immediate answers,” said Jiwon Yang, a Ph.D. student at KAIST. “I was also able to ask questions I would’ve hesitated to bring up with a human TA, which led me to ask even more and ultimately improved my understanding of the course.”   KAIST (President Kwang Hyung Lee) announced on June 5th that a joint research team led by Prof. Yoonjae Choi of the Kim Jaechul Graduate School of AI and Prof. Hwajeong Hong of the Department of Industrial Design has successfully developed and deployed a Virtual Teaching Assistant (VTA) that provides personalized feedback to individual students even in large-scale classes.   This study marks one of the first large-scale, real-world deployments in Korea, where the VTA was introduced in the “Programming for Artificial Intelligence” course at the KAIST Kim Jaechul Graduate School of AI, taken by 477 master’s and Ph.D. students during the Fall 2024 semester, to evaluate its effectiveness and practical applicability in an actual educational setting.   The AI teaching assistant developed in this study is a course-specialized agent, distinct from general-purpose tools like ChatGPT or conventional chatbots. The research team implemented a Retrieval-Augmented Generation (RAG) architecture, which automatically vectorizes a large volume of course materials—including lecture slides, coding assignments, and video lectures—and uses them as the basis for answering students’ questions.       < Photo 2. Teaching Assistant demonstrating to the student how the Virtual Teaching Assistant works>   When a student asks a question, the system searches for the most relevant course materials in real time based on the context of the query, and then generates a response. This process is not merely a simple call to a large language model (LLM), but rather a material-grounded question answering system tailored to the course content—ensuring both high reliability and accuracy in learning support.   Sunjun Kweon, the first author of the study and head teaching assistant for the course, explained, “Previously, TAs were overwhelmed with repetitive and basic questions—such as concepts already covered in class or simple definitions—which made it difficult to focus on more meaningful inquiries.” He added, “After introducing the VTA, students began to reduce repeated questions and focus on more essential ones. As a result, the burden on TAs was significantly reduced, allowing us to concentrate on providing more advanced learning support.”   In fact, compared to the previous year’s course, the number of questions that required direct responses from human TAs decreased by approximately 40％.       < Photo 3. A student working with VTA. >   The VTA, which was operated over a 14-week period, was actively used by more than half of the enrolled students, with a total of 3,869 Q&A interactions recorded. Notably, students without a background in AI or with limited prior knowledge tended to use the VTA more frequently, indicating that the system provided practical support as a learning aid, especially for those who needed it most.   The analysis also showed that students tended to ask the VTA more frequently about theoretical concepts than they did with human TAs. This suggests that the AI teaching assistant created an environment where students felt free to ask questions without fear of judgment or discomfort, thereby encouraging more active engagement in the learning process.   According to surveys conducted before, during, and after the course, students reported increased trust, response relevance, and comfort with the VTA over time. In particular, students who had previously hesitated to ask human TAs questions showed higher levels of satisfaction when interacting with the AI teaching assistant.       < Figure 1. Internal structure of the AI Teaching Assistant (VTA) applied in this course. It follows a Retrieval-Augmented Generation (RAG) structure that builds a vector database from course materials (PDFs, recorded lectures, coding practice materials, etc.), searches for relevant documents based on student questions and conversation history, and then generates responses based on them. >   Professor Yoonjae Choi, the lead instructor of the course and principal investigator of the study, stated, “The significance of this research lies in demonstrating that AI technology can provide practical support to both students and instructors. We hope to see this technology expanded to a wider range of courses in the future.”   The research team has released the system’s source code on GitHub, enabling other educational institutions and researchers to develop their own customized learning support systems and apply them in real-world classroom settings.       < Figure 2. Initial screen of the AI Teaching Assistant (VTA) introduced in the "Programming for AI" course. It asks for student ID input along with simple guidelines, a mechanism to ensure that only registered students can use it, blocking indiscriminate external access and ensuring limited use based on students. >     The related paper, titled “A Large-Scale Real-World Evaluation of an LLM-Based Virtual Teaching Assistant,” was accepted on May 9, 2025, to the Industry Track of ACL 2025, one of the most prestigious international conferences in the field of Natural Language Processing (NLP), recognizing the excellence of the research.       < Figure 3. Example conversation with the AI Teaching Assistant (VTA). When a student inputs a class-related question, the system internally searches for relevant class materials and then generates an answer based on them. In this way, VTA provides learning support by reflecting class content in context. >   This research was conducted with the support of the KAIST Center for Teaching and Learning Innovation, the National Research Foundation of Korea, and the National IT Industry Promotion Agency.

KAIST Research Team Develops Electronic Ink for Ro..

A team of researchers from KAIST and Seoul National University has developed a groundbreaking electronic ink that enables room-temperature printing of variable-stiffness circuits capable of switching between rigid and soft modes. This advancement marks a significant leap toward next-generation wearable, implantable, and robotic devices.       < Photo 1. (From left) Professor Jae-Woong Jeong and PhD candidate Simok Lee of the School of Electrical Engineering, (in separate bubbles, from left) Professor Gun-Hee Lee of Pusan ​​National University, Professor Seongjun Park of Seoul National University, Professor Steve Park of the Department of Materials Science and Engineering>   Variable-stiffness electronics are at the forefront of adaptive technology, offering the ability for a single device to transition between rigid and soft modes depending on its use case. Gallium, a metal known for its high rigidity contrast between solid and liquid states, is a promising candidate for such applications. However, its use has been hindered by challenges including high surface tension, low viscosity, and undesirable phase transitions during manufacturing.   On June 4th, a research team led by Professor Jae-Woong Jeong from the School of Electrical Engineering at KAIST, Professor Seongjun Park from the Digital Healthcare Major at Seoul National University, and Professor Steve Park from the Department of Materials Science and Engineering at KAIST introduced a novel liquid metal electronic ink. This ink allows for micro-scale circuit printing – thinner than a human hair – at room temperature, with the ability to reversibly switch between rigid and soft modes depending on temperature.   The new ink combines printable viscosity with excellent electrical conductivity, enabling the creation of complex, high-resolution multilayer circuits comparable to commercial printed circuit boards (PCBs). These circuits can dynamically change stiffness in response to temperature, presenting new opportunities for multifunctional electronics, medical technologies, and robotics.   Conventional electronics typically have fixed form factors – either rigid for durability or soft for wearability. Rigid devices like smartphones and laptops offer robust performance but are uncomfortable when worn, while soft electronics are more comfortable but lack precise handling. As demand grows for devices that can adapt their stiffness to context, variable-stiffness electronics are becoming increasingly important.       < Figure 1. Fabrication process of stable, high-viscosity electronic ink by dispersing micro-sized gallium particles in a polymer matrix (left). High-resolution large-area circuit printing process through pH-controlled chemical sintering (right). >   To address this challenge, the researchers focused on gallium, which melts just below body temperature. Solid gallium is quite stiff, while its liquid form is fluid and soft. Despite its potential, gallium’s use in electronic printing has been limited by its high surface tension and instability when melted.   To overcome these issues, the team developed a pH-controlled liquid metal ink printing process. By dispersing micro-sized gallium particles into a hydrophilic polyurethane matrix using a neutral solvent (dimethyl sulfoxide, or DMSO), they created a stable, high-viscosity ink suitable for precision printing. During post-print heating, the DMSO decomposes to form an acidic environment, which removes the oxide layer on the gallium particles. This triggers the particles to coalesce into electrically conductive networks with tunable mechanical properties.   The resulting printed circuits exhibit fine feature sizes (~50 μm), high conductivity (2.27 × 10⁶ S/m), and a stiffness modulation ratio of up to 1,465 – allowing the material to shift from plastic-like rigidity to rubber-like softness. Furthermore, the ink is compatible with conventional printing techniques such as screen printing and dip coating, supporting large-area and 3D device fabrication.       < Figure 2. Key features of the electronic ink. (i) High-resolution printing and multilayer integration capability. (ii) Batch fabrication capability through large-area screen printing. (iii) Complex three-dimensional structure printing capability through dip coating. (iv) Excellent electrical conductivity and stiffness control capability.>   The team demonstrated this technology by developing a multi-functional device that operates as a rigid portable electronic under normal conditions but transforms into a soft wearable healthcare device when attached to the body. They also created a neural probe that remains stiff during surgical insertion for accurate positioning but softens once inside brain tissue to reduce inflammation – highlighting its potential for biomedical implants.       < Figure 3. Variable stiffness wearable electronics with high-resolution circuits and multilayer structure comparable to commercial printed circuit boards (PCBs). Functions as a rigid portable electronic device at room temperature, then transforms into a wearable healthcare device by softening at body temperature upon skin contact.>   “The core achievement of this research lies in overcoming the longstanding challenges of liquid metal printing through our innovative technology,” said Professor Jeong. “By controlling the ink’s acidity, we were able to electrically and mechanically connect printed gallium particles, enabling the room-temperature fabrication of high-resolution, large-area circuits with tunable stiffness. This opens up new possibilities for future personal electronics, medical devices, and robotics.”       < Figure 4. Body-temperature softening neural probe implemented by coating electronic ink on an optical waveguide structure. (Left) Remains rigid during surgery for precise manipulation and brain insertion, then softens after implantation to minimize mechanical stress on the brain and greatly enhance biocompatibility. (Right) >   This research was published in Science Advances under the title, “Phase-Change Metal Ink with pH-Controlled Chemical Sintering for Versatile and Scalable Fabrication of Variable Stiffness Electronics.” The work was supported by the National Research Foundation of Korea, the Boston-Korea Project, and the BK21 FOUR Program.

RAIBO Runs over Walls with Feline Agility... Ready..

  < Photo 1. Research Team Photo (Professor Jemin Hwangbo, second from right in the front row) >   KAIST's quadrupedal robot, RAIBO, can now move at high speed across discontinuous and complex terrains such as stairs, gaps, walls, and debris. It has demonstrated its ability to run on vertical walls, leap over 1.3-meter-wide gaps, sprint at approximately 14.4 km/h over stepping stones, and move quickly and nimbly on terrain combining 30° slopes, stairs, and stepping stones. RAIBO is expected to be deployed soon for practical missions such as disaster site exploration and mountain searches.   Professor Jemin Hwangbo's research team in the Department of Mechanical Engineering at our university announced on June 3rd that they have developed a quadrupedal robot navigation framework capable of high-speed locomotion at 14.4 km/h (4m/s) even on discontinuous and complex terrains such as walls, stairs, and stepping stones.   The research team developed a quadrupedal navigation system that enables the robot to reach its target destination quickly and safely in complex and discontinuous terrain.   To achieve this, they approached the problem by breaking it down into two stages: first, developing a planner for planning foothold positions, and second, developing a tracker to accurately follow the planned foothold positions.   First, the planner module quickly searches for physically feasible foothold positions using a sampling-based optimization method with neural network-based heuristics and verifies the optimal path through simulation rollouts.   While existing methods considered various factors such as contact timing and robot posture in addition to foothold positions, this research significantly reduced computational complexity by setting only foothold positions as the search space. Furthermore, inspired by the walking method of cats, the introduction of a structure where the hind feet step on the same spots as the front feet further significantly reduced computational complexity.       < Figure 1. High-speed navigation across various discontinuous terrains >   Second, the tracker module is trained to accurately step on planned positions, and tracking training is conducted through a generative model that competes in environments of appropriate difficulty.   The tracker is trained through reinforcement learning to accurately step on planned plots, and during this process, a generative model called the 'map generator' provides the target distribution.   This generative model is trained simultaneously and adversarially with the tracker to allow the tracker to progressively adapt to more challenging difficulties. Subsequently, a sampling-based planner was designed to generate feasible foothold plans that can reflect the characteristics and performance of the trained tracker.   This hierarchical structure showed superior performance in both planning speed and stability compared to existing techniques, and experiments proved its high-speed locomotion capabilities across various obstacles and discontinuous terrains, as well as its general applicability to unseen terrains.   Professor Jemin Hwangbo stated, "We approached the problem of high-speed navigation in discontinuous terrain, which previously required a significantly large amount of computation, from the simple perspective of how to select the footprint positions. Inspired by the placements of cat's paw, allowing the hind feet to step where the front feet stepped drastically reduced computation. We expect this to significantly expand the range of discontinuous terrain that walking robots can overcome and enable them to traverse it at high speeds, contributing to the robot's ability to perform practical missions such as disaster site exploration and mountain searches."   This research achievement was published in the May 2025 issue of the international journal Science Robotics.   Paper Title: High-speed control and navigation for quadrupedal robots on complex and discrete terrain, (https://www.science.org/doi/10.1126/scirobotics.ads6192)   YouTube Link: https://youtu.be/EZbM594T3c4?si=kfxLF2XnVUvYVIyk  

Professor Hyun Myung＇s Team Wins First Place in a ..

  < Photo 1. (From left) Daebeom Kim (Team Leader, Ph.D. student), Seungjae Lee (Ph.D. student), Seoyeon Jang (Ph.D. student), Jei Kong (Master's student), Professor Hyun Myung >   A team of the Urban Robotics Lab, led by Professor Hyun Myung from the KAIST School of Electrical Engineering, achieved a remarkable first-place overall victory in the Nothing Stands Still Challenge (NSS Challenge) 2025, held at the 2025 IEEE International Conference on Robotics and Automation (ICRA), the world's most prestigious robotics conference, from May 19 to 23 in Atlanta, USA.   The NSS Challenge was co-hosted by HILTI, a global construction company based in Liechtenstein, and Stanford University's Gradient Spaces Group. It is an expanded version of the HILTI SLAM (Simultaneous Localization and Mapping)＊ Challenge, which has been held since 2021, and is considered one of the most prominent challenges at 2025 IEEE ICRA.   ＊SLAM: Refers to Simultaneous Localization and Mapping, a technology where robots, drones, autonomous vehicles, etc., determine their own position and simultaneously create a map of their surroundings.       < Photo 2. A scene from the oral presentation on the winning team's technology (Speakers: Seungjae Lee and Seoyeon Jang, Ph.D. candidates of KAIST School of Electrical Engineering) >   This challenge primarily evaluates how accurately and robustly LiDAR scan data, collected at various times, can be registered in situations with frequent structural changes, such as construction and industrial environments. In particular, it is regarded as a highly technical competition because it deals with multi-session localization and mapping (Multi-session SLAM) technology that responds to structural changes occurring over multiple timeframes, rather than just single-point registration accuracy.   The Urban Robotics Lab team secured first place overall, surpassing National Taiwan University (3rd place) and Northwestern Polytechnical University of China (2nd place) by a significant margin, with their unique localization and mapping technology that solves the problem of registering LiDAR data collected across multiple times and spaces. The winning team will be awarded a prize of ＄4,000.       < Figure 1. Example of Multiway-Registration for Registering Multiple Scans >   The Urban Robotics Lab team independently developed a multiway-registration framework that can robustly register multiple scans even without prior connection information. This framework consists of an algorithm for summarizing feature points within scans and finding correspondences (CubicFeat), an algorithm for performing global registration based on the found correspondences (Quatro), and an algorithm for refining results based on change detection (Chamelion). This combination of technologies ensures stable registration performance based on fixed structures, even in highly dynamic industrial environments.       < Figure 2. Example of Change Detection Using the Chamelion Algorithm>   LiDAR scan registration technology is a core component of SLAM (Simultaneous Localization And Mapping) in various autonomous systems such as autonomous vehicles, autonomous robots, autonomous walking systems, and autonomous flying vehicles.   Professor Hyun Myung of the School of Electrical Engineering stated, "This award-winning technology is evaluated as a case that simultaneously proves both academic value and industrial applicability by maximizing the performance of precisely estimating the relative positions between different scans even in complex environments. I am grateful to the students who challenged themselves and never gave up, even when many teams abandoned due to the high difficulty."       < Figure 3. Competition Result Board, Lower RMSE (Root Mean Squared Error) Indicates Higher Score (Unit: meters)>   The Urban Robotics Lab team first participated in the SLAM Challenge in 2022, winning second place among academic teams, and in 2023, they secured first place overall in the LiDAR category and first place among academic teams in the vision category.

KAIST to Develop a Korean-style ChatGPT Platform S..

On May 23rd, KAIST (President Kwang-Hyung Lee) announced that its Digital Bio-Health AI Research Center (Director: Professor JongChul Ye of KAIST Kim Jaechul Graduate School of AI) has been selected for the Ministry of Science and ICT's 'AI Top-Tier Young Researcher Support Program (AI Star Fellowship Project).' With a total investment of ₩11.5 billion from May 2025 to December 2030, the center will embark on the full-scale development of AI technology and a platform capable of independently inferring and determining the kinds of diseases, and discovering new drugs.       < Photo. On May 20th, a kick-off meeting for the AI Star Fellowship Project was held at KAIST Kim Jaechul Graduate School of AI’s Yangjae Research Center with the KAIST research team and participating organizations of Samsung Medical Center, NAVER Cloud, and HITS. [From left to right in the front row] Professor Jaegul Joo (KAIST), Professor Yoonjae Choi (KAIST), Professor Woo Youn Kim (KAIST/HITS), Professor JongChul Ye (KAIST), Professor Sungsoo Ahn (KAIST), Dr. Haanju Yoo (NAVER Cloud), Yoonho Lee (KAIST), HyeYoon Moon (Samsung Medical Center), Dr. Su Min Kim (Samsung Medical Center) >   This project aims to foster an innovative AI research ecosystem centered on young researchers and develop an inferential AI agent that can utilize and automatically expand specialized knowledge systems in the bio and medical fields.   Professor JongChul Ye of the Kim Jaechul Graduate School of AI will serve as the lead researcher, with young researchers from KAIST including Professors Yoonjae Choi, Kimin Lee, Sungsoo Ahn, and Chanyoung Park, along with mid-career researchers like Professors Jaegul Joo and Woo Youn Kim, jointly undertaking the project. They will collaborate with various laboratories within KAIST to conduct comprehensive research covering the entire cycle from the theoretical foundations of AI inference to its practical application.     Specifically, the main goals include: - Building high-performance inference models that integrate diverse medical knowledge systems to enhance the precision and reliability of diagnosis and treatment. - Developing a convergence inference platform that efficiently combines symbol-based inference with neural network models. - Securing AI technology for new drug development and biomarker discovery based on 'cell ontology.'     Furthermore, through close collaboration with industry and medical institutions such as Samsung Medical Center, NAVER Cloud, and HITS Co., Ltd., the project aims to achieve:   - Clinical diagnostic AI utilizing medical knowledge systems. - AI-based molecular target exploration for new drug development. - Commercialization of an extendible AI inference platform.   Professor JongChul Ye, Director of KAIST's Digital Bio-Health AI Research Center, stated, "At a time when competition in AI inference model development is intensifying, it is a great honor for KAIST to lead the development of AI technology specialized in the bio and medical fields with world-class young researchers." He added, "We will do our best to ensure that the participating young researchers reach a world-leading level in terms of research achievements after the completion of this seven-year project starting in 2025."   The AI Star Fellowship is a newly established program where post-doctoral researchers and faculty members within seven years of appointment participate as project leaders (PLs) to independently lead research. Multiple laboratories within a university and demand-side companies form a consortium to operate the program.   Through this initiative, KAIST plans to nurture bio-medical convergence AI talent and simultaneously promote the commercialization of core technologies in collaboration with Samsung Medical Center, NAVER Cloud, and HITS.

“For the First Time, We Shared a Meaningful Exchan..

• KAIST team up with NAVER AI Lab and Dodakim Child Development Center Develop ‘AAcessTalk’, an AI-driven Communication Tool bridging the gap Between Children with Autism and their Parents   • The project earned the prestigious Best Paper Award at the ACM CHI 2025, the Premier International Conference in Human-Computer Interaction   • Families share heartwarming stories of breakthrough communication and newfound understanding.       < Photo 1. (From left) Professor Hwajung Hong and Doctoral candidate Dasom Choi of the Department of Industrial Design with SoHyun Park and Young-Ho Kim of Naver Cloud AI Lab >   For many families of minimally verbal autistic (MVA) children, communication often feels like an uphill battle. But now, thanks to a new AI-powered app developed by researchers at KAIST in collaboration with NAVER AI Lab and Dodakim Child Development Center, parents are finally experiencing moments of genuine connection with their children.   On the 16th, the KAIST (President Kwang Hyung Lee) research team, led by Professor Hwajung Hong of the Department of Industrial Design, announced the development of ‘AAcessTalk,’ an artificial intelligence (AI)-based communication tool that enables genuine communication between children with autism and their parents.   This research was recognized for its human-centered AI approach and received international attention, earning the Best Paper Award at the ACM CHI 2025＊, an international conference held in Yokohama, Japan.   ＊ACM CHI (ACM Conference on Human Factors in Computing Systems) 2025: One of the world's most prestigious academic conference in the field of Human-Computer Interaction (HCI).   This year, approximately 1,200 papers were selected out of about 5,000 submissions, with the Best Paper Award given to only the top 1％. The conference, which drew over 5,000 researchers, was the largest in its history, reflecting the growing interest in ‘Human-AI Interaction.’   Called AACessTalk, the app offers personalized vocabulary cards tailored to each child’s interests and context, while guiding parents through conversations with customized prompts. This creates a space where children’s voices can finally be heard—and where parents and children can connect on a deeper level.   Traditional augmentative and alternative communication (AAC) tools have relied heavily on fixed card systems that often fail to capture the subtle emotions and shifting interests of children with autism. AACessTalk breaks new ground by integrating AI technology that adapts in real time to the child’s mood and environment.       < Figure. Schematics of AACessTalk system. It provides personalized vocabulary cards for children with autism and context-based conversation guides for parents to focus on practical communication. Large ‘Turn Pass Button’ is placed at the child’s side to allow the child to lead the conversation. >     Among its standout features is a large ‘Turn Pass Button’ that gives children control over when to start or end conversations—allowing them to lead with agency. Another feature, the “What about Mom/Dad?” button, encourages children to ask about their parents’ thoughts, fostering mutual engagement in dialogue, something many children had never done before.   One parent shared, “For the first time, we shared a meaningful exchange.” Such stories were common among the 11 families who participated in a two-week pilot study, where children used the app to take more initiative in conversations and parents discovered new layers of their children’s language abilities.   Parents also reported moments of surprise and joy when their children used unexpected words or took the lead in conversations, breaking free from repetitive patterns. “I was amazed when my child used a word I hadn’t heard before. It helped me understand them in a whole new way,” recalled one caregiver.   Professor Hwajung Hong, who led the research at KAIST’s Department of Industrial Design, emphasized the importance of empowering children to express their own voices. “This study shows that AI can be more than a communication aid—it can be a bridge to genuine connection and understanding within families,” she said.   Looking ahead, the team plans to refine and expand human-centered AI technologies that honor neurodiversity, with a focus on bringing practical solutions to socially vulnerable groups and enriching user experiences.   This research is the result of KAIST Department of Industrial Design doctoral student Dasom Choi's internship at NAVER AI Lab.   ＊ Thesis Title: AACessTalk: Fostering Communication between Minimally Verbal Autistic Children and Parents with Contextual Guidance and Card Recommendation   ＊ DOI: 10.1145/3706598.3713792   ＊ Main Author Information: Dasom Choi (KAIST, NAVER AI Lab, First Author), SoHyun Park (NAVER AI Lab) , Kyungah Lee (Dodakim Child Development Center), Hwajung Hong (KAIST), and Young-Ho Kim (NAVER AI Lab, Corresponding Author)   This research was supported by the NAVER AI Lab internship program and grants from the National Research Foundation of Korea: the Doctoral Student Research Encouragement Grant (NRF-2024S1A5B5A19043580) and the Mid-Career Researcher Support Program for the Development of a Generative AI-Based Augmentative and Alternative Communication System for Autism Spectrum Disorder (RS-2024-00458557).

Decoding Fear： KAIST Identifies An Affective Brain..

Even after the COVID-19 pandemic, various new infectious diseases continue to emerge, posing ongoing viral threats that demand robust and sustained immune defenses. However, excessive immune reactions can also harm body tissues, causing significant health issues. KAIST and an international research team have discovered a critical protein that acts as a 'switch' regulating immune responses to viruses. This breakthrough is expected to lay the groundwork for future infectious disease responses and autoimmune disease treatment strategies.   KAIST (President Kwang-Hyung Lee) announced on May 14 that a joint research team led by Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering at KAIST and Professor Seunghee Cha from University of Florida has discovered the mechanism by which double-stranded RNA derived from mitochondria amplifies immune responses. They identified the protein SLIRP as an 'immune switch' that regulates this process, playing a crucial role in both viral infections and autoimmune diseases.       < (From left) Master's candidate Yewon Yang, Professor Yoosik Kim and Ph.D. candidate Doyeong Ku of the Department of Chemical and Biomolecular Engineering >   Autoimmune diseases arise when the immune system fails to differentiate between external pathogens and the body's own molecules, leading to self-directed attacks. Despite extensive research, the precise causes of excessive inflammatory conditions like Sjögren’s syndrome and systemic lupus erythematosus remain unclear, and effective treatments are still limited.   To uncover the molecular mechanisms driving immune hyperactivation and to identify potential regulatory factors, the research team led by Professor Yoosik Kim focused on mitochondrial double-stranded RNA (mt-dsRNA), a genetic immunogenic material produced within cellular organelles. Since mt-dsRNA structurally resembles viral RNA, it can mistakenly trigger immune responses even in the absence of an actual viral infection.   The team discovered that SLIRP, a key regulator of mt-dsRNA, amplifies immune responses by stabilizing the RNA. They confirmed that SLIRP expression increases in experimental models simulating the tissues of autoimmune disease patients and viral infections. Conversely, suppressing SLIRP significantly reduced the immune response, underscoring its role as a critical factor in immune amplification.   This study also demonstrated the dual function of SLIRP in different contexts. In cells infected with human beta coronavirus OC43 and encephalomyocarditis virus (EMCV), SLIRP suppression led to reduced antiviral responses and increased viral replication. Meanwhile, in the blood and salivary gland cells of Sjögren’s syndrome patients, where both SLIRP and mt-dsRNA levels were elevated, suppressing SLIRP alleviated the abnormal immune response.   These findings highlight SLIRP as a key molecular switch that regulates immune responses in both infections and autoimmune diseases.       < Figure 1. Schematic diagram of antiviral signal amplification by SLIRP: SLIRP-based mt-dsRNA induction, cytoplasmic accumulation, and strong interferon response induction by positive feedback of immune response activation. Confirmation of the immune regulatory function of SLIRP in defense against autoimmune diseases Sjögren's syndrome, coronavirus, and encephalomyocarditis virus infection. >   Professor Yoosik Kim remarked, "Through this study, we have identified SLIRP as a crucial protein that drives immune amplification via mt-dsRNAs. Given its dual role in autoimmune diseases and viral infections, SLIRP presents a promising target for immune regulation therapies across various inflammatory disease contexts."   The study, with Ph.D. student Do-Young Ku (first author) and M.S. student Ye-Won Yang (second author) from the Department of Chemical and Biomolecular Engineering at KAIST as primary contributors, was published online in the journal Cell Reports on April 19, 2025.   ※ Paper title: SLIRP amplifies antiviral signaling via positive feedback regulation and contributes to autoimmune diseases   ※ Main authors: Do-Young Ku (KAIST, first author), Ye-Won Yang (KAIST, second author), Seunghee Cha (University of Florida, corresponding author), Yoosik Kim (KAIST, corresponding author)   This study was supported by the Ministry of Health and Welfare's Public Health Technology Research Program and the National Institutes of Health (NIH) through Research Project (R01) funding.

KAIST ＆ CMU Unveils Amuse, a Songwriting AI-Collab..

Wouldn't it be great if music creators had someone to brainstorm with, help them when they're stuck, and explore different musical directions together? Researchers of KAIST and Carnegie Mellon University (CMU) have developed AI technology similar to a fellow songwriter who helps create music.   KAIST (President Kwang-Hyung Lee) has developed an AI-based music creation support system, Amuse, by a research team led by Professor Sung-Ju Lee of the School of Electrical Engineering in collaboration with CMU. The research was presented at the ACM Conference on Human Factors in Computing Systems (CHI), one of the world’s top conferences in human-computer interaction, held in Yokohama, Japan from April 26 to May 1. It received the Best Paper Award, given to only the top 1％ of all submissions.       < (From left) Professor Chris Donahue of Carnegie Mellon University, Ph.D. Student Yewon Kim and Professor Sung-Ju Lee of the School of Electrical Engineering >   The system developed by Professor Sung-Ju Lee’s research team, Amuse, is an AI-based system that converts various forms of inspiration such as text, images, and audio into harmonic structures (chord progressions) to support composition.   For example, if a user inputs a phrase, image, or sound clip such as “memories of a warm summer beach”, Amuse automatically generates and suggests chord progressions that match the inspiration.   Unlike existing generative AI, Amuse is differentiated in that it respects the user's creative flow and naturally induces creative exploration through an interactive method that allows flexible integration and modification of AI suggestions.   The core technology of the Amuse system is a generation method that blends two approaches: a large language model creates music code based on the user's prompt and inspiration, while another AI model, trained on real music data, filters out awkward or unnatural results using rejection sampling.       < Figure 1. Amuse system configuration. After extracting music keywords from user input, a large language model-based code progression is generated and refined through rejection sampling (left). Code extraction from audio input is also possible (right). The bottom is an example visualizing the chord structure of the generated code. >   The research team conducted a user study targeting actual musicians and evaluated that Amuse has high potential as a creative companion, or a Co-Creative AI, a concept in which people and AI collaborate, rather than having a generative AI simply put together a song.   The paper, in which a Ph.D. student Yewon Kim and Professor Sung-Ju Lee of KAIST School of Electrical and Electronic Engineering and Carnegie Mellon University Professor Chris Donahue participated, demonstrated the potential of creative AI system design in both academia and industry.   ※ Paper title: Amuse: Human-AI Collaborative Songwriting with Multimodal Inspirations DOI: https://doi.org/10.1145/3706598.3713818   ※ Research demo video: https://youtu.be/udilkRSnftI?si=FNXccC9EjxHOCrm1   ※ Research homepage: https://nmsl.kaist.ac.kr/projects/amuse/   Professor Sung-Ju Lee said, “Recent generative AI technology has raised concerns in that it directly imitates copyrighted content, thereby violating the copyright of the creator, or generating results one-way regardless of the creator’s intention. Accordingly, the research team was aware of this trend, paid attention to what the creator actually needs, and focused on designing an AI system centered on the creator.”   He continued, “Amuse is an attempt to explore the possibility of collaboration with AI while maintaining the initiative of the creator, and is expected to be a starting point for suggesting a more creator-friendly direction in the development of music creation tools and generative AI systems in the future.”   This research was conducted with the support of the National Research Foundation of Korea with funding from the government (Ministry of Science and ICT). (RS-2024-00337007)