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KAIST Illuminates the Eyes of Humanoid Robots with Minimal Memory
<CVPR 2026 poster session. From left to right: Minseok Seo (KAIST, first author), Mark Hamilton (MIT and Microsoft, second author), and Prof. Changick Kim (KAIST, corresponding author)>
From facial recognition on smartphones to humanoid robots, computer vision technology, which serves as the eyes of artificial intelligence (AI), is widely utilized in our daily lives. A joint research team from KAIST and international institutions has developed a technology that allows AI to see the world more clearly with minimal memory, increasing GPU (Graphics Processing Unit) memory efficiency by up to 16 times. This achievement is evaluated as a core technology that will accelerate the era of humanoid robots and on-device AI.
<Overview of Upsample Anything. Given a high-resolution image, it is first downsampled to a low-resolution image and then reconstructed through test-time optimization (TTO). During this process, pixel-wise anisotropic kernel parameters are learned. The learned kernels are subsequently applied to low-resolution foundation feature maps to generate high-resolution feature maps. These feature maps are then used to perform pixel-wise anisotropic Joint Bilateral Upsampling, enabling high-quality reconstruction at high resolution>
KAIST announced on June 17th that a research team led by Professor Changick Kim from the School of Electrical Engineering, through joint research with researchers from MIT and Microsoft in the United States, has developed 'Upsample Anything,' a universal technology that can enhance the visual performance of AI even with limited GPU memory.
Following its acceptance to 'CVPR 2026,' the world's most prestigious conference in the field of artificial intelligence and computer vision, this achievement was awarded the 'CVPR Compute Gold Star' in recognition of its efficient utilization of computational resources. It was also selected as the 'Transparency Champion,' ranking first overall in the category of research process transparency and reproducibility. This is an accomplishment that widely recognizes the core elements of responsible AI research, including research performance, computational resources used, code disclosure, and experimental reproducibility.
Recently, humanoid robots, autonomous driving systems, and AI based on world models (AI models that learn and predict the physical environment and changes of the real world) have been compressing input images into low-resolution features (core information extracted from images by AI) to increase computational speed and reduce memory usage.
However, during the compression process, a problem occurs where important visual information, such as small objects, thin structures, and minute defects, is lost. Conversely, processing all images at high resolution from the beginning requires massive GPU memory and computational resources, making real-time processing difficult. This has remained an unresolved challenge for a long time in situations where small devices like smartphones or robots, where mobility is crucial, must precisely perceive their surrounding environment.
To overcome these limitations, the research team developed a training-free (requiring no additional data training) upsampling technology that restores low-resolution feature information into high resolution by utilizing the edge and structural information of the input image.
Existing technologies required a separate retraining or complex optimization process to be applied to new environments or data. In contrast, 'Upsample Anything' developed by the research team can find the optimal restoration method using just a single input image, allowing it to be immediately applied to various environments.
In addition, by compressing and utilizing only core information instead of storing and processing all visual information at high resolution, GPU memory usage was significantly reduced. Based on a 224×224 size image (approximately 50,000 pixels) widely used in AI research, the research team restored visual information close to the original with a short calculation of about 0.4 seconds, achieving a performance that improves GPU memory efficiency by up to 16 times.
This means that artificial intelligence can perceive its surrounding environment more precisely even with limited computational resources. Therefore, this technology is expected to be widely used in various next-generation artificial intelligence fields, such as small devices like smartphones, as well as humanoid robots that need to accurately identify and manipulate small objects, autonomous driving systems, and on-device AI.
<Comparison image illustrating the performance gap with conventional methods (AI-generated). Conventional vision foundation models understand a scene by converting the input image into low-resolution features at a small patch level (left). Upsample Anything restores these low-resolution features to the original resolution level, enabling the AI to comprehend the scene's structure and boundaries with significantly higher precision (right)>
Professor Changick Kim said, “This technology is an algorithm that can significantly increase the visual precision of artificial intelligence with fewer resources, and it is expected to accelerate the commercialization of humanoid robots and on-device AI.” He added, “It is even more meaningful because it was recognized at CVPR not only for its performance but also for its computational efficiency and research transparency.”
This research was participated in by KAIST PhD student Minseok Seo as the first author, and this achievement was presented on June 7 at 'CVPR 2026,' the world's most prestigious conference in the field of artificial intelligence and computer vision.
※ Paper Title: Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling, DOI:10.48550/arXiv.2511.16301
※ Author Information: Minseok Seo (KAIST, First Author), Mark Hamilton (MIT, Microsoft, Second Author), Changick Kim (KAIST, Corresponding Author)
2026.06.17 View 107 -
KAIST Breaks a Major AI Bottleneck with Liquid Cooling Technology 10 Times More Efficient Than the Previous Record
<(From Left) Professor Sung Jin Kim, Professor Ikjin Lee, Dr. Yong Jin Lee, Ph.D candidate Hansol Lee, Ph.D candidate ChulHyun Hwang>
AI data centers are often described as “power-hungry giants.” Not only do artificial intelligence computations consume enormous amounts of electricity, but a significant amount of energy is also required to cool the semiconductor chips that heat up during operation. As AI chips continue to deliver higher performance, the amount of heat they generate is increasing rapidly. As a result, conventional air cooling and external copper heat spreaders are approaching their practical limits. To address this challenge, KAIST research team has developed an ultra-high-efficiency liquid-cooling technology that cools semiconductor chips from within.
KAIST (President Kwang Hyung Lee) announced on the 16th that a joint research team led by Professor Sung Jin Kim of the Department of Mechanical Engineering and Professor Ikjin Lee of the School of AI and Computing has developed a highly efficient liquid-cooling technology that directly cools high-heat-flux semiconductor chips using room-temperature water. The researchers achieved this by embedding liquid-cooling channels, thinner than a human hair, directly inside a silicon semiconductor chip. The team successfully maintained the chip temperature below 100°C even under extreme heat-generation conditions exceeding 2,000 watts per square centimeter (W/cm²).
<Manifold Microchannel Cooling Device Structure for Cooling High-Heat-Generation Semiconductor Chips>
The researchers focused on a Manifold MicroChannel (MMC hereafter) structure embedded directly inside a silicon chip. Microchannel cooling removes heat through microscopic fluid channels that are smaller than a human hair. In conventional designs, coolant must travel through numerous microchannels from one end of the chip to the other. This long flow path increases flow resistance and requires greater pumping power to circulate the coolant.
The manifold structure developed by the research team distributes coolant through multiple inlet channels and collects it through multiple outlets. An analogy can be drawn to a logistics network: instead of shipping all goods from a single origin to a distant destination, multiple distribution centers are strategically placed to shorten transportation distances. Because the coolant travels only a short distance within each channel, flow resistance is significantly reduced, and the required pumping pressure becomes much lower.
Previous MMC designs often suffered from uneven coolant distribution, with some channels receiving too much coolant while others received too little. The research team overcame this challenge by optimizing the structure so that coolant could flow evenly through all microchannels. Through extensive design analysis and advanced simulations, they identified an optimized cooling architecture that delivers high cooling performance while minimizing energy loss.
The optimized structure was then fabricated in an actual silicon semiconductor chip and experimentally validated. Under the same temperature-rise condition, the cooling system achieved a coefficient of performance (COP) of 106,000. This is approximately ten times higher than the previous world-leading result of around 10,000 reported in Nature (van Erp et al.) in 2020. In practical terms, it means that only about one-tenth of the pumping power is required to remove the same amount of heat.
Notably, this performance was achieved without relying on phase-change cooling, nanoscale surface modifications, or expensive materials such as diamond. Ordinary room-temperature water was used as the coolant. In addition, the device was fabricated using a low-temperature process below 350°C that is compatible with conventional semiconductor manufacturing. This means the technology could be implemented in existing semiconductor foundries without requiring major additional equipment investments.
<Fabricated Cooling Device, Experimental Results, and Applications>
The technology is expected to help address thermal management challenges in a wide range of high-heat-flux electronic systems, including AI accelerators, high-performance computing (HPC) systems, three-dimensional semiconductor packaging, power electronics, and defense electronics. In particular, data centers are increasingly constrained not only by computing performance but also by cooling power consumption and cooling infrastructure requirements. Technologies that reduce pumping power at the chip level could therefore play an important role in improving the energy efficiency of next-generation data centers and alleviating thermal bottlenecks.
<Research Image(AI-generated)>
Professor Sung Jin Kim said, “As the performance of AI semiconductors and advanced electronic packaging becomes increasingly limited by heat, we expect this technology to serve as a foundational cooling solution for future high-performance computing systems.”
The study was co-first-authored by Young Jin Lee, ChulHyun Hwang, and Hansol Lee from the Department of Mechanical Engineering at KAIST. The research was published on June 15 in the international journal Energy Conversion and Management.
Paper title: Highly energy-efficient manifold microchannel for cooling electronics with a coefficient of performance over 100,000
DOI: 10.1016/j.enconman.2026.121422
This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (Grant No. 2021R1A2C3011944), and by the Specialized Research Laboratory Program for Ultra-High-Heat-Flux Cooling Systems of the Korea Research Institute for Defense Technology Planning and Advancement (KRIT), funded by the Defense Acquisition Program Administration (Grant No. KRIT-CT-22-022).
2026.06.16 View 259 -
A Breakthrough Concept in Nano-Printing Technology
<(From Left) Professor Inkyu Park, Ph.D candidate Byung-Ho Kang, Researcher Jun-Ho Jeong, Professor Junseong Ahn>
A breakthrough technology has been developed that allows metal circuits floating on water to be transferred directly onto any desired surface. A South Korean research team has introduced a novel technique capable of transferring ultra-fine nano-circuits onto plant leaves and fruits, as well as curved automotive surfaces and robot exteriors, all without causing any damage. This technology is expected to be widely utilized across various cutting-edge industries, including smart agriculture, wearable healthcare, and bioelectronics.
KAIST announced on June 15th that a joint research team led by Distinguished Professor Inkyu Park from the Department of Mechanical Engineering, Dr. Jun-Ho Jeong from the Korea Institute of Machinery and Materials and Professor Junseong Ahn from Korea University has successfully developed "Water-Floating Nano-Transfer Printing (WF-nTP)." This technology enables precision metal thin films floated on water to be transferred onto various 3D surfaces.
Conventional nano-transfer printing (nTP), which is widely used to manufacture electronic devices and sensors, typically requires high heat, intense pressure, adhesives, or toxic chemical solvents. Consequently, applying this method to biological tissues or complex curved surfaces that are sensitive to heat and pressure has proven highly challenging.
To overcome these limitations, the research team proposed a completely new approach: floating metal circuits on the surface of water.
Fabrication: The team deposited an ultra-thin layer of metals such as gold (Au), platinum (Pt), palladium (Pd), or nickel (Ni) onto a polymer mold.
Separation: They then used plasma gas which has a high-energy state of ionized gas to selectively remove (etch) a part of the mold.
Floating: When this structure is placed in water, water infiltrates through the microscopic gaps, causing the 20-nanometer (nm) thick metal film to float to the surface on its own while perfectly maintaining its original shape.
<Overview of the water-floated nano mesh and versatile transfer technique>
The research team transferred the metal circuits using a "scooping" method, which involves submerging the target object beneath the floating film and slowly lifting it upward. As the water evaporates, capillary force (the force that moves liquid through narrow spaces) tightly adheres the circuit to the surface. Once the water completely dries, intermolecular forces come into play, securing the circuit firmly in place without the need for any adhesive.
Notably, the team also succeeded in transferring circuits onto hydrophobic (water-repellent) surfaces, such as lotus leaves. By adding a small amount of ethanol to the water to lower its surface tension (the property of a liquid surface that causes it to shrink), they effectively overcame a major limitation of conventional technologies.
This technology holds immense potential for widespread application because it can adapt to diverse surfaces while flawlessly preserving the nano-patterns. Using this method, the research team fabricated Surface-Enhanced Raman Scattering (SERS) sensors which are used for high-sensitivity detection of trace chemical substances and attached them directly to plant leaves and fruit surfaces. Through this, they successfully detected thiram, a pesticide component, on the surfaces of leaves and fruits. Furthermore, they successfully implemented a wearable, high-performance hydrogen gas sensor by transferring a palladium (Pd) mesh onto highly flexible thermoplastic polyurethane (TPU) fibers.
<Research Image(AI-generated)>
Distinguished Professor Inkyu Park stated: "This technology is highly significant as it shatters the substrate limitations of conventional nano-transfer printing, allowing nano-patterns to be transferred onto sensitive surfaces like living plant leaves or human skin without heat or adhesives. We expect it to evolve into a core platform technology for wearable sensors and bioelectronics, finding applications in smart agriculture for pesticide detection without damaging crops, wearable health monitoring devices, bioelectronic devices, and electronic skins for next-generation robots."
This research, with PhD student Byung-Ho Kang from the KAIST Department of Mechanical Engineering participating as the first author, was published online on March 30, 2026, in the prestigious international academic journal Nature Communications.
The study authors include Byung-Ho Kang, Ji-Hwan Ha, Yeongjae Kwon, Sohee Jeon, Donho Lee, Byeongmin Kang, Soon Hyoung Hwang, Junseong Ahn, Jun-Ho Jeong, and Inkyu Park.
Meanwhile, this research was conducted with funding from the Ministry of Science and ICT through the National Research Foundation of Korea (NRF) Mid-Career Researcher Support Program, and the Ministry of Trade, Industry and Energy through the Korea Evaluation Institute of Industrial Technology (KEIT) Alchemist Project.
2026.06.15 View 188 -
How Small Can Semiconductors Get? KAIST Develops Atomic-Level Prediction Technology
<(From Left) Dr. Tae Hyung Kim, Dr. Juho Lee, (Upper Left) Professor Yong-Hoon Kim>
As the global semiconductor industry enters the so-called "2 nm (nanometer, one-billionth of a meter) process" era, the actual size of transistors — the core components of semiconductor chips — still remains above 10 nm. How much smaller, then, can transistors actually get? KAIST researchers have developed a technology to predict that limit through quantum mechanical atom-level calculations.
KAIST (President Kwang Hyung Lee) announced on the 14th that a research team led by Professor Yong-Hoon Kim of the School of Electrical Engineering has developed a computational design technology that utilizes computer simulations to analyze and predict the scaling limits of transistors, a key challenge in developing next-generation semiconductor devices.
<Research Image(AI-generated)>
Transistors are ultra-small switches that turn electrical currents on and off, serving as key components that determine the performance and power efficiency of semiconductor chips that power smartphones, artificial intelligence computers, and more. The semiconductor industry has continuously downsized transistors to achieve higher performance and lower power consumption. However, when the size becomes excessively small, quantum tunneling occurs—a quantum mechanical phenomenon where electrons pass through energy barriers they normally cannot cross—making current control difficult. For this reason, identifying how much smaller transistors can be made within the boundaries of quantum tunneling is a critical task in next-generation semiconductor development.
However, it is virtually impossible to experimentally confirm the scaling limits of transistors directly. With current technology, it is difficult to precisely control and quantitatively analyze the contact area where the metal electrode and the semiconductor channel (the pathway through which current flows inside a transistor) meet at the atomic level.
The research team resolved this issue by utilizing ab initio or first-principles calculations, a method that computes material properties based solely on fundamental physics laws without relying on experimental data. The research team had previously developed and reported a new theoretical-computational framework called multi-space constrained-search density functional theory (MS-DFT), which extends the scope of first-principles calculations from materials to devices by precisely analyzing the complex quantum phenomena occurring at the interface where metal electrodes and semiconductors meet and across which electrons flow.
In this study, the team built on this framework to perform computational transfer length method (TLM) experiments, the gold standard experimental technique for extracting contact resistance (the resistance to current flow occurring at the metal electrode-semiconductor interface). Based on the atomic-level TLM calculations results, they identified the quantum tunneling limit (the length at which electrons stop leaking and begin to allow transistor current control).
The research team applied this technology to a monolayer MoS₂ (molybdenum disulfide) device, a representative two-dimensional semiconductor material that can be made as thin as an atomic layer and is a candidate material for next-generation transistor channels. As a result, they were able to quantitatively analyze how deeply electrons penetrate into the channel and how much this hinders current flow control depending on the type of metal electrode and the contact atomic geometry. In other words, they clarified that the limit to how small a transistor can be made varies depending on which metal and contact structure are selected. This implies that the performance and limits of a device can now be predicted in advance solely through computer simulations before the actual transistor fabrication.
< Analysis of Contact Resistance and Critical Tunneling Length in Two-Dimensional Semiconductors Using the First-Principles Transfer Length Method >
According to the research results, the critical tunneling length—the maximum length at which electrons penetrate into the channel and begin to affect transistor operation—was found not to be a single fixed value. This length emerged as a design variable that changes depending on the work function of the metal (the minimum energy required to remove an electron from a metal) and the contact structure of the interface where the metal and semiconductor meet. This signifies that the extent to which a transistor can be downsized depends on the combination of materials and structural design.
In particular, among the candidate metal types and contact structures considered, the research team confirmed that the length where electrons stop leaking could be reduced to less than 4 nm. This result demonstrates the possibility of making transistors even smaller than the levels achieved today.
Furthermore, the research team proposed a design strategy for next-generation semiconductor chips that reduce power consumption by combining two-dimensional semiconductors with different properties.
This study is significant because it establishes a platform for predicting scaling limits and designing optimal device configurations before actually fabricating semiconductor chips. Through this, it is expected to reduce trial and error and shorten the development period in the process of developing next-generation ultra-small AI semiconductor devices.
Professor Yong-Hoon Kim said, "This study is significant because it presents a new physical criterion for defining how small next-generation transistors can become. By computationally analyzing quantum mechanical phenomena in the sub-10 nm regime, which are difficult to probe experimentally, we have opened a path toward utilizing these findings in next-generation transistor design."
The study, in which Dr. Tae Hyung Kim participated as the first author, was published online on May 28th in the prestigious computational journal 'npj Computational Materials, a prestigious journal in the field of computational materials science ※ Title of the paper: Ab initio transfer length method simulations of tunneling limits in 2D semiconductors, DOI: https://doi.org/10.1038/s41524-026-02101-1
This research was conducted with support from programs such as the Mid-Career Researcher Program and EDISON 2.0 Program of the National Research Foundation of Korea.
2026.06.15 View 198 -
"What if there is no one to farm? KAIST reveals a hidden risk to future food security
<(From Left) Professor Nicklas Forsell, Professor Hyungjun Kim, Ph.D candidate Hongtak Lee, Professor Haewon Chon>
The cause of future food shortages may not be a lack of farmland, but a shortage of agricultural workforce." Amid the reality of low birth rates and rural extinction, a joint international research team from KAIST has developed a new data-driven model that reflects the decline in the agricultural workforce into the analysis of future food security (the ability to stably produce and supply food required by the public). The research findings show that in the future, a shortage of agricultural workforce could act as a key constraint on farmland utilization in most regions of the world.
KAIST announced on June 12th that a research team led by Professor Hyungjun Kim from the Department of AI Future (adjunct at the Moon Soul Graduate School of Future Strategy), in joint research with Professor Haewon Chon from the KI Institute for Climate, Environment, and Energy (Graduate School of Green Growth and Sustainability), Professor Nicklas Forsell, and Professor Taikan Oki from the University of Tokyo in Japan, analyzed the impact of the agricultural workforce decline on future food production.
< Regional Farmland Supply, Demand, and Shortage Outlook for 2030 and 2100 >
Until now, food security and climate change research have mainly focused on "how much farmland can be secured." The approach was to predict the future by calculating whether the climate and soil are suitable for farming and how much food demand will increase in the future.
However, the research team asked a different question: "What if there is farmland, but no one to farm it?" In fact, as low birth rates and urban concentration manifest in many countries, the rural population is declining. As economies develop, there is also a stronger tendency for people to move from agriculture to the manufacturing or service sectors. The research team determined that these changes could have a significant impact on future food production.
The research team performed the analysis using five future scenarios that combine SSP (Shared Socioeconomic Pathways) and RCP (Representative Concentration Pathways), which are representative international scenario frameworks that predict how future society and climate change will unfold. SSP is a scenario that assumes the direction of societal changes such as population growth, economic growth, and technological development, while RCP is a scenario that shows how the future climate will change depending on greenhouse gas emissions.
The research team newly reflected the agricultural workforce variable into these future outlooks. While previous predictions were mainly based on the land available for farming and food demand, this study simultaneously considered the actual number of people who will farm. In other words, the reality that food production can be limited if the agricultural workforce is insufficient, even if farmland and climate conditions are adequate, was reflected in the model.
The results of the analysis were even clearer than expected. In the future, it was shown that the farmland area that can actually be utilized will decrease due to the shortage of agricultural workforce in most regions of the world. In some regions, the lack of agricultural workforce was analyzed to act as a greater limiting factor than climate or soil.
The research team explained that the agricultural workforce problem may not be easily resolved even in a future where technological development occurs rapidly. Technological development increases the cultivable area per person. However, as industries grow, more people move to the manufacturing and service sectors, which conversely accelerates the decline of the rural population, leading to a reduced workforce and a phenomenon where farmland utilization becomes more restricted. These results suggest the importance of a sustainable development model.
In addition, it was confirmed that if international migration is restricted, developed countries will experience a shortage of agricultural workforce, while conversely, the agricultural population in some low-income countries may increase excessively. This shows that migration policies are also closely linked to food security. Professor Hyungjun Kim explained, "This study analyzed future food issues by considering not only climate and land, but also changes in people. It is a study showing that realistic social problems such as low birth rates and the avoidance of rural areas can have a significant impact on future food security and climate change responses."
This study, in which Ph.D. student Hongtak Lee from the Moon Soul Graduate School of Future Strategy participated as the first author and Professor Hyungjun Kim from the Department of AI Future conducted as the corresponding author, was published on June 1 in the international academic journal 'Nature Sustainability'. Furthermore, in recognition of its academic importance, the study was prominently highlighted in a separate commentary titled "Farming needs more hands" (News & Views; https://doi.org/10.1038/s41893-026-01841-8) in the same journal. The commentary evaluated this research as "a first step that shifted the conventional question of 'how much land is there' to whether there are enough people and productivity per worker to cultivate that land." ※ Title of Paper: Agricultural Workforce as a Potential Bottleneck of Future Cropland Availability, DOI: https://doi.org/10.1038/s41893-026-01824-9 ※ Main Authors: Hongtak Lee (KAIST, First Author), Nicklas Forsell (KAIST), Taikan Oki (University of Tokyo), Haewon Chon (KAIST), Hyungjun Kim (KAIST, Corresponding Author)
<Research Image(AI-generated)>
This research was conducted with the support of the AI-based Future Climate Technology Development Framework Program, the Brain Pool Program, and the Plus Project (Ministry of Science and ICT) through the National Research Foundation of Korea.
2026.06.15 View 168 -
"KAIST to Produce 'Janus-Faced' Nanomaterials... Paving the Way for New Materials to Selectively Capture Radioactive Pollutants"
<(From Left) Professor Ho Jin Ryu, Dr. Hyun Woo Seong, Dr. Minseok Lee>
The way has been paved for the development of multi-functional materials for applications such as removing radioactive pollutants and shielding electromagnetic waves. A KAIST research team has succeeded, for the first time in the world, in synthesizing the core raw material for fabricating asymmetric MXene, a so-called "Janus-faced" nanomaterial that can implement distinct functions due to differing atomic compositions on its two sides.
<AI-Generated Research Image>
KAIST announced on June 11th that a research team led by Professor Ho Jin Ryu from the Department of Nuclear and Quantum Engineering has successfully synthesized experimentally an asymmetric layered ceramic (a ceramic with an asymmetric structure where atomic layers are stacked on top of each other), which is a required precursor for fabricating asymmetric MXene (a two-dimensional nanomaterial with different atomic compositions on its two sides).
MXene is a two-dimensional nanomaterial with excellent electrical conductivity and high surface reactivity, drawing significant attention in various advanced technology sectors including energy storage devices and sensors. However, the MXenes developed so far possess a symmetric structure with identical atomic compositions on both sides, which has limited the functions they can implement.
In contrast, asymmetric MXenes have different atomic compositions on their two sides, allowing each side to perform distinct functions. This asymmetry enables the emergence of new properties that are difficult to achieve with conventional symmetric-structured materials. In particular, it is expected to be utilized in developing next-generation functional materials, such as adsorption filters for removing radionuclides and materials for absorbing and shielding electromagnetic waves.
Until now, however, the existence of asymmetric MXene had mostly been suggested only through computer simulations, and its actual implementation remained difficult because the raw materials required for manufacturing had not been secured.
To solve this problem, the research team applied a high-entropy material design strategy (a material design approach that mixes multiple elements to achieve new properties). By simultaneously mixing six elements—titanium (Ti), zirconium (Zr), hafnium (Hf), tantalum (Ta), aluminum (Al), and tin (Sn)—they discovered that a stable asymmetric structure, in which the composition of the outer metal atomic layers is arranged differently due to differences in atomic size, forms naturally. This is evaluated as a new structure-forming mechanism that has never been reported in conventional MXene raw materials.
The asymmetric layered ceramic synthesized by the research team acts as a precursor (a raw material for making the final material) that can be converted into asymmetric MXene with different atomic compositions on its two sides when subjected to chemical etching (a process that selectively removes only specific atomic layers).
< Experimental Observations of the Asymmetric Ceramic Structure Synthesized in This Study >
This achievement holds great significance as it establishes the foundation for actually implementing asymmetric MXene, which had previously remained confined to theory. In particular, it presents the possibility of expanding into various advanced technology fields that were difficult to achieve with existing symmetric structures, such as radionuclide capturing, electromagnetic wave shielding, sensors, and piezoelectric devices (devices that convert pressure or vibration into electrical energy).
The research team has currently filed patent applications in South Korea, the United States, and Japan for the asymmetric layered ceramic and the asymmetric MXene utilizing it. They plan to verify the actual radioactive ion removal performance and electromagnetic wave shielding performance through follow-up studies.
Professor Ho Jin Ryu said, "This study is an instance of realizing an asymmetric atomic structure, which was difficult to achieve using conventional crystallography, through a high-entropy material design strategy. We expect that it can be developed into a core original technology in the fields of safety and the environment, such as radionuclide capturing and electromagnetic wave shielding, in the future."
Dr. Minseok Lee of KAIST (currently at the Korea Atomic Energy Research Institute) participated as the first author, and Dr. Hyun Woo Seong of KAIST (currently at the Korea Atomic Energy Research Institute) participated as a co-author. The study was published in the world-renowned scientific journal 'Nature Communications' on April 30. ※ Paper Title: An Asymmetrically Out-of-Plane Ordered MAX Phase as a Precursor for Janus MXenes, DOI : 10.1038/s41467-026-72561-y
Meanwhile, this research was conducted with support from the Nuclear Energy Basic Research Support Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.
2026.06.11 View 320 -
"Age of Robots Making Human-Like Judgments, KAIST Solves Key Challenge in Physical AI
< (From left) Professor Chang D. Yoo, Tung M. Luu (PhD candidate, first author) at the back center, and Hwanhee Kim (M.S candidate, second author) at the front right >
“Robots that make judgments like humans are coming faster than we think.” A core technology that will accelerate the era where robots understand human intentions and choose the correct actions on their own has been developed in South Korea. KAIST researchers solved a key challenge in the commercialization of physical AI by developing a technology where AI learns human judgment criteria on its own with just a few videos. KAIST announced on June 10th that a research team led by Professor Chang D. Yoo from the School of Electrical Engineering has developed 'VOTP (Video-based Optimal TransPort Preference)' for the first time in the world, a new technology that allows AI to learn human intentions and judgment criteria using just a few preference videos instead of thousands to tens of thousands of human evaluation data points.
< VOTP Overview Diagram >
The research team's paper has been accepted to ICML (International Conference on Machine Learning) 2026, the world's most prestigious AI conference, which will be held at COEX in Seoul this July. It was selected for an Oral presentation, an honor given to only the top 0.7% (168 papers) out of all submitted papers (23,918 papers), recognizing the excellence of the research. ICML is considered one of the most influential international conferences in the fields of AI and machine learning. Recently, AI technology is rapidly evolving beyond generative AI that writes text and draws pictures into the era of 'Physical AI,' which moves actual machines and acts in the real world. Representative examples include robots that perform dangerous tasks in factories instead of humans, autonomous vehicles that judge road situations on their own, and medical robots that perform delicate surgeries. However, there was a barrier that had to be overcome for the practical application of physical AI. It is the problem of learning human-level evaluation criteria to judge whether the actions performed by a machine match human intentions and which actions are more desirable. For example, when a surgical robot performs suturing or an autonomous vehicle passes through a complex intersection, the AI must choose the most appropriate action among numerous options. To achieve this, a 'Reward Function' that reflects human preferences and judgment criteria is required. However, until now, humans had to directly evaluate thousands to tens of thousands of action data points to build this, which required an enormous amount of time and cost. The research team focused on the way humans learn new tasks after seeing just a few demonstrations. VOTP, developed by the research team, helps AI understand human-preferred action patterns on its own with just a few videos of good and bad examples. Even without humans evaluating a vast amount of data one by one as before, AI can understand human judgment criteria and expand its learning to various situations. The core idea of this research is that intelligent machines such as robots or autonomous vehicles can quickly grasp human intents with only a small number of videos containing human preferences. The algorithm developed for this purpose proved its effectiveness and generalization performance through extensive experiments across various environments and tasks. This method can significantly reduce human feedback and data construction costs required for physical AI development. Since robots, autonomous vehicles, and industrial machinery can learn actions that meet human expectations with only a small number of examples, it is expected to drastically shorten development time and costs. The technology can be widely applied not only to robot arm control, humanoid robots, autonomous vehicles, smart factories, drones, and surgical robots, but also to AI agents that directly operate computers. In particular, it is expected to be utilized as a core foundational technology for all physical AI systems that need to learn human intention and satisfaction.
< VOTP Research Image (AI Generated) >
Professor Chang D. Yoo said, "The core of physical AI is making machines understand human intentions and choose the correct actions," and added, "Since VOTP can learn human judgment criteria with only a small number of videos, it is a core technology that will accelerate the era of robots making human-like judgments." This research, in which PhD student Tung M. Luu from the School of Electrical Engineering participated as the first author, was selected as an Oral presentation paper at ICML (International Conference on Machine Learning) 2026, the world's most prestigious AI conference. ※ Paper Title: Video-Based Optimal Transport for Feedback-Efficient Offline Preference-Based Reinforcement Learning, Paper File: https://sanctusfactory.com/data/file/publications/202606091714078906.pdf This research was conducted with support from the Institute for Information & Communication Technology Planning & Evaluation (IITP) and the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT."
2026.06.10 View 510 -
KAIST Launches Mind Care & Growth Center, an Integrated Mental Health Platform for the AI Era
<Human Behavior and Mental Health Symposium Poster>
KAIST announced the official launch of the KAIST Mind Care & Growth Center (KMCG), a new integrated platform that strengthens mental health support for students and faculty while advancing digital mental health research. To mark the occasion, KAIST hosted an international symposium titled "Human Behavior and Mental Health" on June 10, 2026, at the Cho Su-mi Hall in the Chang Young Shin Student Activity Center on its main Daejeon campus.
The symposium drew significant interest from both academia and the public, with pre-registration reaching capacity within just one week of opening. More than 300 KAIST faculty members, researchers, students, and global digital health experts attended, underscoring the urgent demand for advanced mental health frameworks in the era of artificial intelligence.
A Unified Hub for Integrated Mental Health Support
The KMCG consolidates psychological counseling, psychiatric care, and crisis intervention services that were previously dispersed across campus, expanding and reorganizing the existing student counseling center. By eliminating the inconvenience of navigating multiple support channels, the center provides more systematic, seamless, and consistent care under one roof.
Going beyond a traditional counseling center, the KMCG serves as a living laboratory that fuses real-world mental health expertise with KAIST's research capabilities. Researchers from artificial intelligence, brain science, industrial design, digital humanities, mathematics, and computer science collaborate to develop and evaluate new approaches to mental health support — delivering evidence-based interventions to students, gathering practice-based insights, and continuously improving services.
Keynote: Global Collaboration with UCSF Neuroscape
The keynote address was delivered by Professor Adam Gazzaley, Founder and Executive Director of Neuroscape at the University of California, San Francisco (UCSF), and co-founder of Akili Interactive, developer of EndeavorRx — the world's first FDA-authorized prescription video game treatment.
Professor Gazzaley presented the latest advances in digital mental health, including VR-based cognitive training and multimodal biosensing. Neuroscape's interdisciplinary model — integrating clinical neuroscience, engineering, design, and AI — stands as a leading example of the convergence research that KMCG seeks to promote, and serves as a benchmark for building a multidisciplinary mental health innovation ecosystem at KAIST.
In subsequent lightning talk sessions, KAIST faculty from mathematics, brain engineering, AI, computer science, industrial design, and digital humanities presented future research directions and discussed opportunities for global collaboration with Neuroscape.
New Research: Generative AI as a Clinically Ambivalent Technology
In parallel with its service mission, the KMCG is already generating impactful scientific insights. Center Director Dooyoung Jung recently co-authored a study with Professor Chul-Hyun Cho of Korea University College of Medicine, published on June 2, 2026, in the Journal of Medical Internet Research (JMIR), examining the impact of generative AI on psychiatric practice.
<(From Left) Ph. D candidate Myungsung Kim, Professor Yoosuk An, Professor Dooyoung Jung, Professor Chul-Hyun Cho>
Based on the real-world clinical experiences of 311 Korean psychiatrists, the study found that generative AI may help organize emotions, support self-care, and improve access to treatment — but may also create risks such as overdependence, reinforcement of distorted beliefs, and potentially dangerous situations depending on patient vulnerability and context of use.
<An AI-generated image of a psychiatrist consulting with a patient who uses AI.>
The research team described generative AI as a "clinically ambivalent technology" and emphasized that it should assist, not replace, human therapists. Safe adoption requires technological reliability, clinical validation, crisis response systems, and robust governance by medical professionals.
<Structure of Item-by-Item Topic Analysis and Co-occurrence Analysis>
<Key Connections Among Clinical Signals, Interpretations, and Priority Implementation Tasks for Generative AI>
Leadership Perspectives
Dooyoung Jung, Director of the KMCG, stated: "A healthy mind is the foundation for achieving outstanding research. We will develop the Mind Care & Growth Center into a platform that leads mental health solutions for universities across Korea."
KAIST President Kwang Hyung Lee remarked: "In an era where artificial intelligence is replacing even high-level human intellectual labor, our greatest concern is not technological deficiency, but the potential erosion of human-centered values and culture. Technology must remain a tool guided by human wisdom and philosophy — not the other way around."
As AI becomes more deeply embedded in everyday life, the KMCG is positioned to address new mental health challenges arising from AI dependency while harnessing digital technology in ways that support human flourishing. Pre-registration for the symposium has closed, but interested members of the public were welcome to attend on-site. Some services and amenities were limited.
Related Publication
Title: Mapping Practice-Based Signals of Generative AI in Psychiatric Care: Qualitative Study of Korean Psychiatrists' Experiences, Interpretations, and Implementation Priorities
Journal: Journal of Medical Internet Research (JMIR), June 2, 2026
DOI: 10.2196/96556
Authors: Myungsung Kim (UNIST, co-first author), Yoosuk An (Seoul National University / National Traffic Rehabilitation Hospital, co-first author), Chul-Hyun Cho (Korea University, co-corresponding author), Dooyoung Jung (KAIST, co-corresponding author)
2026.06.10 View 311 -
KAIST Develops Hydrogel Material with Improved Skin Adhesion and Controllable Degradation Rate
<(From Left) Researcher Han-Yeol Yang, Professor Haeshin Lee>
Could wound healing dressings adhere better, and drug delivery patches become more sophisticated? A KAIST research team has developed a technology that leverages natural ingredients derived from plants to increase the strength of seaweed-based hydrogel (a gel material that contains a large amount of water while maintaining its shape) by more than fivefold, while also controlling its adhesiveness and degradation rate.
KAIST announced on June 9th that a research team led by Professor Haeshin Lee from the Department of Chemistry has developed a new material design strategy that utilizes tannic acid—a type of polyphenol, which is a natural antioxidant abundant in tea and fruits—to enhance the mechanical strength and adhesiveness of seaweed-derived hydrogel and to control its degradation rate.
Hydrogel is a high-moisture gel material used in contact lenses, acne patches, mask packs, and wound healing dressings. Because it can adhere closely to the skin while holding drugs or active ingredients, it is being utilized in various bio and healthcare fields, such as drug delivery systems (materials that effectively deliver drugs to desired sites), wound dressings (medical dressings that protect wounds and aid healing), tissue engineering scaffolds (structures that help regenerate artificial tissue), and cosmetic materials.
Among various hydrogel materials, the research team focused on 'κ-Carrageenan'. κ-Carrageenan is a natural polymer extracted from red seaweed (rhodophytes) such as agar-agar, and it is a familiar food ingredient used to increase the viscosity and maintain the shape of jellies and sauces. However, there were limitations to improving the performance of hydrogels made with κ-Carrageenan. The κ-Carrageenan molecule contains many structures called sulfate groups, which create intermolecular repulsion—much like magnets of the same pole pushing each other away—and prevent the formation of a dense structure. For this reason, it was difficult to increase the strength and adhesiveness of the hydrogel or to adjust the degradation rate to a desired level.
To solve this problem, the research team focused on finding a natural substance that could effectively interact with the sulfate groups. As a result, they determined that tannic acid, a natural polyphenol abundant in tea and fruits, could be a promising candidate.
Polyphenols are natural ingredients produced by plants to protect themselves from external environments such as ultraviolet rays or pests, and they have the characteristic of being able to bind with multiple substances simultaneously. In particular, tannic acid has multiple binding sites (galloyl groups), so it was expected to interact strongly with the sulfate groups of κ-Carrageenan and connect the molecules together. The research team believed that this characteristic could be utilized to reinforce the hydrogel structure.
As a result of the study, it was confirmed that the sulfate group, which was previously considered a factor hindering hydrogel formation, actually acts as a core binding site with tannic acid. In other words, the structure that was previously considered a "weakness" played a role in making the hydrogel even firmer upon meeting tannic acid.
< Research Image Related to Polyphenol Interactions >
In fact, the storage modulus (an index representing the firmness and elasticity of a gel) of the κ-Carrageenan hydrogel with added tannic acid was approximately 1,632 Pa, showing an improvement of more than fivefold compared to the pure κ-Carrageenan hydrogel (approximately 294 Pa). This means that the hydrogel can maintain its shape more stably even under external pressure or deformation, demonstrating that it can increase the durability and usability of wound healing dressings or drug delivery patches.
In addition, the research team confirmed that tannic acid stably reinforces the internal network structure (gel network) of the already formed hydrogel, regardless of the point in time when the tannic acid is added. This implies that tannic acid connects molecules at multiple points, allowing the internal structure of the hydrogel to remain consistently firm.
Notably, the research team succeeded in implementing rapid degradability and strong adhesiveness simultaneously. In experiments simulating the human stomach and intestinal environments, the hydrogel containing tannic acid degraded relatively quickly while adhering strongly to the skin and rough surfaces. This means that wound healing dressings will not easily fall off during use but can naturally degrade after completing their role, and drug delivery patches can be utilized to stably deliver drugs for a desired period.
This study is meaningful in that it presented a design principle capable of simultaneously controlling the strength, adhesiveness, and degradation rate of hydrogel using only food-grade natural ingredients without complex chemical synthesis processes. The research team expects this technology to be utilized in various bio and healthcare fields, such as capsules and coating materials for food and functional foods, skin-adhering cosmetics and skincare products, wound dressings, drug delivery patches, and tissue engineering scaffolds.
<Research Image (AI-Generated)>
Professor Haeshin Lee said, "This study is an example showing that the mechanical strength, adhesiveness, and degradation behavior of hydrogel can be designed together using only naturally derived materials," adding, "It can be expanded into a safer and simpler natural polymer gel platform in the fields of food, cosmetics, and biomaterials."
This study, in which PhD student Han-Yeol Yang participated as the first author, was published on April 21st in 'Biomimetics', an international academic journal in the field of biomimetics. ※ Paper Title: Adhesive κ-Carrageenan Hydrogels by Polyphenol Intervention, DOI: 10.3390/biomimetics11040290
Meanwhile, this research was conducted with research funding support from Polyphenol Factory Inc., a faculty-led startup enterprise of KAIST.
2026.06.09 View 507 -
KAIST Resolves Long-standing Challenge of Performance Degradation in Stacked 2D Materials
<(Clockwise from the lower right) Sarah S. Park (KAIST), Geunchan Park (POSTECH, first author), Sangwon Moon (second author), and Jaekyung Yi (third author). (Top) Christopher H. Hendon (University of Oregon, fourth author>
KAIST researchers develop a next-generation 2D conductive material that maintains single-layer electronic properties even when multi-layered, accelerating the commercialization of next-generation electronic and quantum devices.
Two-dimensional (2D) materials, which are significantly thinner than a single sheet of paper, have long drawn attention for their exceptional performance. However, they have faced a critical limitation: their performance degrades significantly when multiple layers are stacked.
A research team at KAIST has successfully resolved this long-standing bottleneck by developing a new conductive material that retains its single-layer electronic characteristics even when stacked in multiple layers. This breakthrough is expected to accelerate the commercialization of next-generation electronic devices and quantum materials.
KAIST (President Kwang-Hyung Lee) announced on June 8th that a research team led by Professor Sarah S. Park from the Department of Chemistry, in collaboration with Professor Christopher H. Hendon from the University of Oregon, has developed a new 2D conductive Metal-Organic Framework (MOF). This novel material maintains high electrical conductivity while minimizing interlayer interference.
Because 2D materials are atomically thin, electrons can move through them at ultra-high speeds, making them prime candidates for next-generation semiconductors and quantum materials. However, for practical applications, multiple layers must be stacked. When this happens, interlayer interactions obstruct electron movement, leading to performance degradation—similar to how cars driving fast on separate roads experience traffic congestion at an intersection. In particular, while 2D conductive MOFs exhibit outstanding performance in their single-layer state, their inherent electronic properties weaken in the bulk state, where multiple layers are piled up.
To solve this problem, the research team focused on the "angle" of alignment to prevent the layers from directly interfering with each other. The newly designed molecular structure ensures that even when multiple layers are stacked, each layer is arranged at a specific angle, minimizing direct face-to-face contact. This operates on a similar principle to stacking a deck of cards with a slight twist rather than flushing them perfectly, preventing them from sticking together. As a result, interlayer interactions were reduced, allowing electrons to move more freely. To achieve this structure, the team designed a triptycene-based molecule and used it to synthesize the new 2D conductive MOF material.
The newly developed material, named Ni₃(HITrip)₂ was found to preserve an electronic structure highly similar to that of a single layer, even in a multi-layered state. Notably, it retained a unique electronic structure (the Dirac band structure of a Kagome lattice) that allows electrons to move rapidly and efficiently. This structure is highly advantageous for achieving high electrical conductivity, enabling electrons to travel at high speeds as if on a highway without complex obstacles. This demonstrates that an electronic structure previously thought to be achievable only in a single layer can now be maintained in actual multi-layered bulk materials.
In fact, this material exhibited a high electrical conductivity of 0.58 S/cm without any additional doping (a process of introducing impurities to enhance electrical properties), proving that excellent electrical performance can be achieved while mitigating interlayer interference.
Through computational modeling and spectroscopic analysis, the research team also uncovered the underlying mechanism behind this high conductivity. They confirmed that within the material, the molecules and metal atoms work cooperatively to facilitate electron transport, creating a stable environment for electron movement.
This study holds great significance as it resolves a long-standing challenge in 2D materials: the phenomenon where "stacking degrades performance." By demonstrating that superior electronic properties previously limited to single layers can be realized in bulk materials, this research marks a vital turning point in connecting fundamental research to practical technology.
The research team anticipates that these findings will be widely utilized in the development of high-performance electronic devices and next-generation energy materials. Furthermore, by opening new possibilities for research into quantum materials and topological materials (next-generation functional materials with unique electron transport properties), this breakthrough is expected to contribute significantly to the advancement of future semiconductor and quantum information technologies. Crucially, because the material retains its excellent electronic properties even when stacked, it will broaden the scope of functional material design required for manufacturing actual devices.
Professor Sarah S. Park stated, "This research demonstrates that 2D electronic structures, which were previously thought to be possible only in single layers, can now be realized in bulk materials. By precisely controlling interlayer interactions, a new pathway will open for implementing diverse quantum properties and electronic characteristics in practical materials."
Ph.D candidate Geunchan Park participated as the first author, alongside co-authors Sangwon Moon, Jaekyung Yi, Christopher H. Hendon, and corresponding author Sarah S. Park. The study was published on April 8th in the Journal of the American Chemical Society (JACS), a prestigious international scientific journal in chemistry.
2026.06.08 View 389 -
KAIST Turns DNA from Genetic Information Carrier into Energy Designer, Improving Catalyst Performance
<Professor Jimin Park(Center), Ph.D candidate Tae Kyoung Lee, Ph. D candidate Sang Yeon Oh>
The fixed idea that DNA is only a molecule that stores genetic information is being challenged. KAIST researchers have developed a technology that controls the chemical environment around catalysts at the nanometer scale by designing DNA sequences, the arrangement of A, T, G, and C that make up genetic information. The team has presented a new catalyst platform that can improve hydrogen production efficiency and increase the yield of desired chemical products by designing DNA much like writing a computer program.
KAIST (President Kwang Hyung Lee) announced on the 21st of May that a research team led by Professor Jimin Park of the Department of Chemical and Biomolecular Engineering has developed a core technology that precisely controls the microscopic chemical environment around catalysts by coating the surface of gold nanoparticles, ultrafine gold particles measuring 1–100 nm, with “single-stranded DNA,”a flexible DNA molecule composed of a single strand that can be designed with a desired length and structure and serves as a nano-coating material for controlling the reaction environment.
In electrochemical reactions, which use electricity to drive chemical reactions and are used for hydrogen production or the manufacture of eco-friendly chemicals, performance is determined not only by the catalyst itself but also by the local reaction environment around the catalyst, such as acidity (pH) and ion distribution. However, conventional approaches have relied on special polymer coating materials, plastic-like materials made of long molecular chains, and have faced limitations in precisely designing internal structures at the nanometer scale.
To solve this problem, the research team focused on “single-stranded DNA,” DNA composed of a single strand. DNA carries a negative charge, meaning it can influence the movement of surrounding ions, atoms or molecules with electric charge, and it has the advantage that its length and base sequence can be freely designed. In particular, changing the base sequence allows the internal network structure of DNA to be precisely controlled, making it possible to create a customized nano-coating layer on the catalyst surface.
< Schematic illustration of catalytic interfacial microenvironment regulation using a single-stranded DNA layer >
The research team attached DNA with various base sequences to the surface of gold nanoparticles and analyzed the electrochemical reactions. As a result, they found that the key factor determining catalyst performance was not simply the thickness of the coating layer, but the internal network structure formed according to the DNA base sequence.
This means that even coating layers of the same thickness can create different pathways for the movement of ions needed for reactions, depending on how the internal DNA structure is organized. It is the same principle as traffic flow changing depending on how a road network is designed, even when the roads are the same width.
The team also used real-time surface-enhanced Raman spectroscopy, a technology that uses lasers to analyze the chemical state of molecules in real time, to observe the reaction process. Through this, they directly confirmed that the DNA layer functions as an interfacial layer, a layer that performs a special function at the boundary where two materials meet, by regulating the movement of hydroxide ions (OH⁻) and changing the local pH around the catalyst.
In simple terms, the DNA layer acts like a “traffic control center” around the catalyst, guiding the movement of ions. It helps some ions move more quickly while restricting the movement of others, thereby changing the reaction environment in the desired direction. By observing this process in real time, the researchers proved that DNA is not merely a protective film, but actively regulates the reaction environment.
The team applied this technology to the hydrogen evolution reaction and the glycerol oxidation reaction, which converts glycerol, a byproduct of biodiesel production, into high-value chemicals. As a result, hydrogen production efficiency varied significantly depending on the DNA base sequence, and the selectivity, the proportion of a specific product formed, for glycerate, a material used in cosmetics and pharmaceuticals, also improved. This means that desired reaction outcomes can be achieved simply by adjusting the DNA sequence, without newly creating complex catalyst structures.
< Research image (AI-generated image) >
Professor Jimin Park said, “This study shows that DNA can be used not as a genetic information storage medium, but as a precise nanomaterial that controls electrochemical reactions,” adding, “By designing DNA sequences to control acidity and ion movement on catalyst surfaces, we expect this technology to be broadly applied across carbon-neutral technologies, including hydrogen production and biomass conversion.”
This study was conducted with KAIST Department of Chemical and Biomolecular Engineering doctoral students Sang Yeon Oh and Tae Kyoung Lee as co-first authors, and Professor Jimin Park as the corresponding author. The research was published on May 5 in the internationally renowned Journal of the American Chemical Society.
※ Paper title: “Programmable Single-Stranded DNA Layers as Modulators of Nanoscale pH at Electrocatalytic Interfaces,” DOI: 10.1021/jacs.6c02995
※ Author information: Sang Yeon Oh and Tae Kyoung Lee (KAIST, co-first authors), Jaeyeon Jun, Jinse Woo, Changho Lee, Yongha Kim (KAIST, co-authors), and Jimin Park (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea’s Outstanding Young Scientist Program , Global Matching Program , and Young Researcher Infrastructure Support Program.
2026.06.08 View 301 -
Humanoid Robot Pilot PIBOT Wins Best Paper Award at the World’s Most Prestigious Robotics Magazine
< (From left of the award recipients) Ph. D candidate Sungjae Min, Ph. D candidate Gyuree Kang, Professor David Hyunchul Shim, Ph.D candidate Hyungjoo Kim >
KAIST announced on June 5th that a paper proposing an aircraft autonomous piloting framework based on the humanoid robot pilot ‘PIBOT,’ developed by a research team led by Professor David Hyunchul Shim of the School of Electrical Engineering, was selected as the Best Paper Award among the papers published in the IEEE Robotics & Automation Magazine (IEEE RAM) in 2025.
< The proposed PIBOT system framework capable of piloting based on aviation manuals and voice communication without modifying existing aircraft >
This award is highly meaningful as it signifies that grassroots research based entirely on domestic, independent initiatives has been recognized as a world-class achievement in robotics. The award ceremony took place in Vienna, Austria, on June 4, 2026 (local time) during the International Conference on Robotics and Automation (ICRA 2026). IEEE Robotics & Automation Magazine (IEEE RAM) is a prestigious academic magazine published by the IEEE Robotics and Automation Society (RAS), under the umbrella of IEEE, the world's largest technical professional organization. It is well known for delivering the latest research achievements, industry trends, and tutorials in the fields of robotics and automation, widely conveying robot technologies applicable to actual industrial sites to researchers in both industry and academia. As of 2025, IEEE RAM recorded an Impact Factor (IF) of 7.1, holding the second highest impact among IEEE publications in the field of robotics. In particular, it presents the Best Paper Award to research that has a significant academic and industrial impact among the papers published after undergoing rigorous peer review. This study was selected as a Future Challenge Defense Technology Research and Development Project by the Agency for Defense Development (ADD) in 2021 and was conducted based purely on domestic technology with support of approximately 5.7 billion won over five years. The research team received high praise for implementing Physical AI technology at an exceptionally high level, enabling a humanoid robot to systematically and adaptively perform complex tasks such as piloting aircraft based on artificial intelligence, going beyond simple walking or carrying items. Recently, humanoid robot technology has been developing rapidly in terms of athletic performance, such as tumbling or implementing complex movements. However, in the industrial sector, the applicability to actual industrial sites is drawing attention as a more critical factor. The pilot robot ‘PIBOT’ being developed by Professor David Hyunchul Shim's research team is designed to acquire specialized knowledge required for aircraft operation and to recognize and respond to actual flight situations in real time, going beyond simple repetitive tasks or logistics processing. Accordingly, it is evaluated as presenting a new direction for the utilization of humanoid robot technology, termed as Expert Physical AI.
< The research team's PIBOT sitting in an actual aircraft (KLA-100) and operating the instruments and control stick >
The research team has successfully completed Phase 1 of the research since the project launched in 2021, and since 2024, they have been developing Phase 2 of the pilot robot, which features a human-like physique and joint structure suitable for actual aircraft piloting. In addition, they are pursuing collaborative research with relevant organizations to expand and apply this technology to various mobile vehicle piloting fields, such as ground vehicles and ships, as well as aircraft.
< PIBOT performing piloting in an aircraft simulator device >
Professor David Hyunchul Shim said, “It is very meaningful that the pilot robot technology, proposed for the first time in the world by Korean researchers, has been recognized as a world-class research achievement thanks to the support of a large-scale national project. We will further develop our research in a direction where humanoid robots can help humans in real-world environments and safely operate complex systems.” In this study, PhD students Sungjae Min, Gyuree Kang, and Hyungjoo Kim participated as co-first authors, and Professor David Hyunchul Shim served as the corresponding author. The paper can be found through IEEE Xplore. ※ Paper Title: “Toward Fully Autonomous Aviation: PIBOT, a Humanoid Robot Pilot for Human-Centric Aircraft Cockpits”, Paper Links: https://doi.org/10.1109/MRA.2024.3505774, https://ieeexplore.ieee.org/document/10798973/ Meanwhile, this research was conducted with support from the Agency for Defense Development's Future Challenge Defense Technology Research and Development Project.
2026.06.05 View 565