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KAIST Names Former Samsung Electronics Chairman and CEO Oh Hyun Kwon as Chair of Its Board of Trustees
KAIST announced that its Board of Trustees elected Oh Hyun Kwon, former Chairman and CEO of Samsung Electronics and Chairman of the Orange Planet startup foundation, as its new chair at the board’s 298th extraordinary meeting in Seoul on September 21, 2026. Prior to the election, Kichul Cha, a KAIST trustee and CEO of InBody, had served as acting chair since the term of former Chair Myung Ja Kim ended in late June.
Kwon earned a bachelor’s degree in electrical engineering from Seoul National University in 1975, a master’s degree in the same field from KAIST in 1977, and a doctorate in electrical engineering from Stanford University in 1985. He joined Samsung in 1985 as a researcher at its Semiconductor Research Institute in the United States. In 1992, he led the development of the world’s first 64Mb DRAM, helping lay the foundation for Samsung’s strategy of building a decisive technological lead over its competitors.
An engineer who rose through the ranks to become a leading business executive, Kwon served as President and Head of Device Solutions Division, Samsung Electronics from 2008 to 2011 and as Vice Chairman and CEO from 2012 to 2017. From 2017 to 2020, he held the positions of Chairman at both Samsung Electronics and the Samsung Advanced Institute of Technology. He is credited with helping propel South Korea's semiconductor industry to global leadership.
After stepping back from corporate management, Kwon served as President of the Korea Invention Promotion Association from 2021 to 2022 and as the seventh Chair of Seoul National University’s Board of Trustees from 2023 to 2025. In September 2025, he became the first business executive to chair the Committee on Mid- to Long-Term Strategy Committee, now under the Ministry of Planning and Budget (then called the Ministry of Economy and Finance). He has also chaired Orange Planet since 2021, supporting young entrepreneurs.
His honors include the Gold Tower Order of Industrial Service Merit in 2009, the Korea Best Scientist and Engineer Award in 2014, the Inchon Award in Industrial Technology category in 2012, and the Dasan Technology Award in 1997. He is also the author of Super Gap (2018) and Again, Super Gap (2020).
Kwon has maintained close ties with his alma mater, KAIST. In 2018, for instance, he became the first KAIST alumnus to receive an honorary doctorate from the university, earning the distinction in business administration. He also helped establish the Kwon Oh Hyun Assistant Professor program, which provides annual support for promising faculty members pursuing creative and ambitious research in physics, mathematics, chemistry, biological sciences, and engineering.
Kwon joined the KAIST Board of Trustees at its 297th extraordinary meeting in August before being elected chair at the September meeting.
His term as chair runs from September 21, 2026, the date of the board resolution, through August 19, 2029, when his term as a trustee expires. Drawing on his experience in building Samsung’s technological leadership, Kwon is expected to help KAIST advance innovation in research and education and further strengthen its global competitiveness.(END)
2026.09.22 View 2624 -
KAIST Develops Smartphone AI That Uses Past Solutions to Tackle Similar Problems
A new AI technology has been developed that retains and reuses knowledge, much like writing down a solution in a notebook and applying it to similar problems rather than asking an expert for help each time. Researchers at KAIST have developed a way for small AI models running on smartphones to store and reuse knowledge from a large server model. The approach reduced server calls by an average of 55.61% compared with a non-cumulative approach while maintaining high accuracy, suggesting that mobile AI could make faster decisions with less server support as it encounters similar problems.
KAIST (President Choongsik Bae) announced on September 21 that a research team led by Professor Jae-Gil Lee from the School of Computing has developed CURE (Cumulative Knowledge Reuse), a technology that enables a small AI model on a device to work efficiently with a large model on a server.
Smartphones have limited processing power and memory, so they typically use small, lightweight AI models. These models can handle simple tasks quickly but may be less accurate when identifying complex or unfamiliar images.
Sending every input to a powerful server model can improve accuracy, but transferring data and waiting for a response takes time. It also adds to network traffic and the server’s computational workload, increasing delays for users and operating costs for service providers.
Researchers have explored a collaborative approach in which the on-device model assesses each input first and sends only difficult cases to the server. Without a way to retain the server’s knowledge, however, the device uses each answer once and may need the same help when a similar input appears. This is much like a student who fails to write down the solution to a difficult problem and has to ask for help again when faced with another of the same kind. The team set out to turn these one-time answers into knowledge the device could continue to use.
CURE first checks whether the on-device model can handle an input reliably. If it cannot, the system consults knowledge previously obtained from the server and stored on the device. It contacts the server only if that knowledge is also insufficient. The process follows three steps, moving from solving a problem independently to consulting previous lessons and, when necessary, asking an expert.
For example, an on-device model that cannot identify a car model in a photo may turn to the server for help. CURE uses the server’s prediction and the image’s features to update the device’s knowledge store. When the device later receives a photo of a similar car model, it can use that knowledge to identify it locally.
CURE does not keep a collection of the original photos processed by the server. Instead, it stores a summary of their shared features and differences. This is like noting a car’s distinguishing features, such as its body shape or headlight design, rather than memorizing the entire photo. The stored knowledge can therefore help the system recognize not only images it has already seen but also similar images it encounters for the first time.
The team tested CURE using vision-language models, which connect images with text to understand visual information. These models link what they see to language, much as people do.
The researchers used MobileCLIP2 on the device and EVA-CLIP, which has 18 billion parameters, on the server. Parameters are numerical values within a model that encode information learned during training. They help the model distinguish objects and identify relationships between them.
In tests on a range of image classification datasets, CURE achieved accuracy close to that of an approach that sends every input to the large server model. It also made an average of 55.61 percent fewer server calls than a device-server collaboration baseline that does not retain knowledge from previous server responses.
In end-to-end tests that included communication time, CURE ran up to 2.80 times as fast as the non-cumulative device-server collaboration baseline and up to 3.67 times as fast as the approach that sends every input to the server. Making fewer server calls reduced the time spent transferring data and waiting for responses.
CURE requires no retraining of either the on-device model or the server model. It leaves both models unchanged and adds a separate store for knowledge obtained from the server, making it adaptable to a range of AI models and services.
The technology could reduce repeated data transfers and server computation image recognition. This could mean shorter waits for users and lower operating costs for service providers.
Potential applications also include robots and wearables that need to recognize their surroundings with limited computing resources. Robots operating over slow or unreliable networks, for example, could use previously acquired knowledge to make more decisions locally without waiting for a server response.
The benefits in practice will depend on the device’s processing power and storage capacity, network conditions, and the characteristics of the input data. Further testing across different devices and environments is therefore needed.
“CURE allows a small on-device AI model to remember and reuse knowledge it has already obtained, rather than repeatedly asking the server the same question,” said Professor Jae-Gil Lee. “By maintaining high accuracy while reducing communication demands and response times, we expect it to help smartphones, robots, and wearables use powerful AI models more efficiently.”
Dr. Youngjun Lee, a postdoctoral researcher from the KAIST Institute of Information Electronics, was the study’s first author, and Professor Jae-Gil Lee from the School of Computing was the corresponding author. Co-authors were Doyoung Kim from Amazon, Junhyeok Kang from LG AI Research, and Professor Hwanjun Song from the KAIST Department of Industrial and Systems Engineering. The findings were presented on September 10 at the European Conference on Computer Vision (ECCV 2026), a leading international computer vision conference held in Malmö, Sweden, from September 8 to 12.
Paper title “CURE: Cumulative Knowledge Reuse for Efficient Device-Server Hybrid Inference in Vision-Language Models”,
DOI: https://doi.org/10.1007/978-3-032-37627-5_21
Author information — Dr. Youngjun Lee (postdoctoral researcher, KAIST Institute of Information Electronics, first author), Doyoung Kim (KAIST graduate, now at Amazon, co-author), Junhyeok Kang (KAIST graduate, now at LG AI Research, co-author), Professor Hwanjun Song (KAIST Department of Industrial and Systems Engineering, co-author), Professor Jae-Gil Lee (KAIST School of Computing, corresponding author)
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2020-II200862, DB4DL: High-Usability and Performance In-Memory Distributed DBMS for Deep Learning, 50% and No. RS-2022-II220157, Robust, Fair, Extensible Data-Centric Continual Learning, 40%), the InnoCORE pro-gram of the Ministry of Science and ICT (AI Meta-Scientist, N10260110, 10%), and Samsung Electronics Co., Ltd.(IO251216-14634-01).
2026.09.21 View 2107 -
KAIST Professor Sunkyu Han Becomes the First Korean Member of the Board of Editors of Organic Syntheses
For more than a century, Organic Syntheses has served as a unique platform beyond conventional peer reviewed journal: before publishing a procedure, its editors personally repeat the experiment in their laboratory to confirm that another chemist's submitted method actually works as reported. Professor Sunkyu Han from KAIST’s Department of Chemistry has now become the first Korean chemist elected as a member of the Board of Editors of Organic Syntheses.
KAIST (President Choongsik Bae) announced on September 11 that Professor Han has been elected as a member of the Board of Editors of Organic Syntheses at the Organic Syntheses corporate meeting, held in Chicago on August 23.
During his five-year term, Professor Han will participate to identify, solicit, and select work for publication in Organic Syntheses. He will also check synthetic procedures submitted by other researchers in his own laboratory to experimentally confirm their reliability and reproducilibity. His term may be extended for up to three additional years.
Founded in 1921, Organic Syntheses follows a publishing model unlike that of most academic journals. A synthetic procedure is published only after the member of the Board of Editors has successfully reproduced it in their laboratory and confirmed the submitted results. The journal is currently led by Editor-in-Chief Professor Rick Danheiser from the Massachusetts Institute of Technology.
Because each procedure undergoes rigorous experimental scrutiny, including the checking of its yield, selectivity, and purification process, the methods published in Organic Syntheses are widely regarded as trusted reference protocols by organic chemists around the world.
The role of a member of the Board of Editors of Organic Syntheses therefore extends well beyond conventional editors. Editors must check other researchers’ procedures and attest to their reliability, a responsibility requiring exceptional scientific judgment and experimental rigor. Over the journal’s history, many of the world’s most distinguished organic chemists have served in this capacity.
The Board of Editors currently comprises 14 members, including the Editor-in-Chief and Associate Editors, and has traditionally included two chemists based in Asia. Distinguished scholars from Japan, China, and Hong Kong have served on the board, among them Ryoji Noyori, winner of the 2001 Nobel Prize in Chemistry. Professor Han, however, is the first Korean chemist elected as a member of the Board of Editors. He will succeed Professor Pauline Chiu of the University of Hong Kong, whose term concludes later this year.
“Professor Sunkyu Han’s election as the first Korean member of the Board of Editors is a significant achievement that reflects both KAIST’s strength in basic science and the growing international stature of Korean organic chemistry,” said KAIST President Choongsik Bae. “KAIST will continue to support its researchers not only for making outstanding discoveries, but also in strengthening the reliability and advancing the frontiers of knowledge across the global academic community.”
Since its founding, Organic Syntheses has published approximately 3,100 articles. Among them, nine have been contributed by organic chemists based in Korea, four of them by KAIST faculty members.
Professor Han’s appointment will also enable KAIST’s Department of Chemistry to host the Organic Syntheses Distinguished Lecture Series during his tenure. Sponsored by Organic Syntheses, Inc., the program will bring internationally renowned organic chemists to KAIST, creating more opportunities for students and early-career researchers to engage directly with leading scholars in the field.
Professor Han is internationally recognized for his work on the chemical synthesis of complex natural products. Since joining KAIST in 2014, he has completed the synthesis of numerous structurally complex alkaloids isolated from Securinega suffruticosa, a medicinal plant native to Korea.
Recently, he has built on this expertise to expand his research into molecular photoswitches, which use light to control molecular structure and function. He is also developing anticancer agents and potential treatments for neurodegenerative disorders, including Alzheimer’s disease, using synthetic derivatives of natural products.
Since June 2025, Professor Han has also served as an Associate Editor of Organic Letters, another leading international journal in the field of organic chemistry.
“Ever since I was a student, I have relied on reactions and procedures published in Organic Syntheses and felt a profound sense of gratitude toward the chemists who personally checked those procedures and turned them into methods the broader community could trust. I have been given this opportunity thanks to everything I have learned while working alongside outstanding group members and colleagues at KAIST. I am deeply grateful to everyone who has supported me throughout that journey, as well as to the university,” said Professor Han.
He added, “I hope to follow in the footsteps of the eminent chemists who helped shape the foundation of modern organic chemistry, including Roger Adams, George Büchi, Arthur Cope, Albert Eschenmoser, Ronald Breslow, and E. J. Corey. By faithfully and diligently carrying out my responsibilities as a member of the Board of Editors of Organic Syntheses, I hope to build a body of reliable chemical knowledge that serves chemists worldwide and contributes to the continued advancement of organic chemistry.”
2026.09.11 View 2437 -
KAIST Develops AI That Can Sense “That’s Not What I Meant” Without Being Told
A future in which AI can recognize a person’s unspoken “that’s not what I meant” response from brain signals and adjust its behavior on its own is coming closer. KAIST researchers have developed a technology that detects cognitive mismatch between humans and AI through brainwaves, enabling AI systems to revise their actions in real time according to human goals. The achievement is expected to accelerate the shift from AI that follows explicit commands to AI that can infer human intent.
KAIST (President Choongsik Bae) announced on September 11 that a research team led by Endowed Chair Professor Sang Wan Lee of the Department of Brain and Cognitive Sciences (Director of the Center for Neuroscience-Inspired Artificial Intelligence), in collaboration with Microsoft Research Asia (MSRA), has developed Neural Value Alignment (NVA), a next-generation brain–computer interface (BCI) technology that uses human brainwaves to align AI behavior with human intentin real time.
For AI to collaborate naturally with people, it must accurately understand what a person actually wants. Until now, however, AI systems have largely inferred human intent from externally observable information such as speech, actions, or gestures.
The challenge is that the same action can reflect different goals, and the same goal can also be achieved through different actions. For example, when a person picks up a cup, it may be unclear whether they intend to drink from it or hand it to someone else. Conversely, if the person's goal is simply to quench their thirst, that same goal could be pursued through several different actions — reaching for the cup, picking up a water bottle, or asking someone else to bring a drink. Because of this two-sided “goal–action ambiguity,” when AI misunderstands human intent, users often have to correct it through additional commands or actions.
To address this problem, the research team focused on the “prediction error” signals that arise unconsciously in the brain when a person encounters an unexpected situation. In simple terms, the team used the brain’s instant “that’s not what I meant” response when AI performs the wrong action or pursues the wrong goal.
The researchers distinguished between two types of brain responses. The first is reward prediction error (RPE), which appears when AI misunderstands the person’s ultimate goal. The second is state prediction error (SPE), which appears when the goal is correct but the process or method of action differs from what the person expected.
The team measured real-time electroencephalography (EEG) signals from people as they observed AI performing tasks. They found that the brain produced different signals depending on whether the AI misunderstood the goal itself or chose the wrong method while pursuing the correct goal. The researchers also identified distinctive brainwave patterns that appeared when both types of errors occurred simultaneously.
By applying deep learning to these brain signals, the team developed a technology that can determine, from EEG alone, how a person is interpreting the AI’s behavior. In other words, even without a person saying “that’s wrong,” the AI can recognize whether the human brain is signaling that “the goal is wrong” or “the method is wrong.”
The team then proposed a Neural Value Alignment-based human–AI synergy algorithm, which feeds these decoded brain signals back to the AI in real time so that it can correct its own behavior.
When the AI detects an SPE signal, it interprets the situation as “the desired goal is correct, but the method is wrong” and adjusts its action strategy. When it detects an RPE signal, it understands that “the goal itself was misunderstood” and searches again for what the person truly intended.
Simulation results showed that the proposed method adapted more quickly than existing approaches even in uncertain situations, such as when a person’s goal suddenly changed or some human neural feedbacks were missing.
The key significance of this research is that it demonstrates the possibility of AI systems correcting themselves by reading a person’s unconscious “that’s not what I meant” brain response, without requiring the user to repeatedly say “do it this way” or “that’s not right.”
As the technology advances, it could be applied to physical AI robots in homes and industrial settings, allowing them to understand user intent more naturally and adjust their actions accordingly. It could also be extended to autonomous vehicles that quickly reflect driver judgment, medical and rehabilitation robots for patients who have difficulty speaking or moving, and educational AI systems that adapt to a student’s cognitive state.
Ultimately, the study presents a new model of human–AI collaboration, moving beyond AI that acts only when explicitly instructed toward AI that can sense human responses and adjust itself accordingly.
Professor Sang Wan Lee, who led the international collaboration, said, “This research is meaningful because it shows that AI can move beyond inferring human intent only from visible behavioral outcomes and instead directly use cognitive signals generated in the brain during collaboration with AI.” He added, “The technology can be expanded to a wide range of fields where human judgment and AI behavior must be closely connected, including physical AI, BCI, autonomous driving, precision personalized education, medical robotics, and human–computer interaction.”
Miran Lee, Director, Microsoft Research Accelerator at Microsoft Research, said, “This achievement is the result of the ongoing international collaboration between KAIST and Microsoft Research Asia. We look forward to continuing this partnership to develop world-class BCI technologies that enable humans and AI to communicate and collaborate more naturally.”
The study’s first author is Xin Xu, a Ph.D. student in KAIST’s Department of Brain and Cognitive Sciences. Researchers from Microsoft Research Asia, including Yansen Wang, Dongqi Han, and Dongsheng Li, also participated in the study. The research was published online in August 2026 in IEEE Transactions on Cybernetics, an international journal in the field of cybernetics.
Paper title: Neural Value Alignment: Human–AI Collaboration Under Goal–Action Ambiguity
DOI: 10.1109/TCYB.2026.3722605
Another KAIST–Microsoft Research Asia collaborative study on helping AI rapidly adapt to continuously changing environments was presented in June at ICML 2026, one of the leading international conferences in artificial intelligence. The study was led by Niklas Koeppe, a Ph.D. student in KAIST’s Program of Brain and Cognitive Engineering, as first author.
Paper title: Mitigating Plasticity Loss through Architectural Design in Continual Learning
Original paper: https://icml.cc/virtual/2026/poster/61534
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Ministry of Science and ICT.
2026.09.10 View 2834 -
KAIST Signs MOU with Université Paris-Saclay on Behalf of K-STAR
KAIST (President Choongsik Bae) announced on September 9 that, representing K-STAR, a consortium comprising KAIST, GIST, DGIST, UNIST, and POSTECH, it signed a memorandum of understanding (MOU) with Université Paris-Saclay on September 7 to strengthen research cooperation. The signing ceremony took place at Bâtiment Bréguet on the university’s campus.
The agreement was concluded on the occasion of President Lee Jae-myung’s state visit to France. It follows up on plans discussed at the ninth Korea-France Joint Committee on Science and Technology, held in Seoul in April, to expand scientific, technological, and academic exchanges between K-STAR and French universities.
Université Paris-Saclay is one of France’s leading research universities. It is home to a large research and education ecosystem encompassing universities, grandes écoles, and major national research organizations, including the French National Centre for Scientific Research (CNRS) and the French Alternative Energies and Atomic Energy Commission (CEA). The university has internationally recognized strengths across a broad range of fields, from mathematics and physics to artificial intelligence, quantum technology, biology, and engineering.
Under the agreement, K-STAR and Université Paris-Saclay will establish a practical framework for cooperation encompassing student exchanges, mobility programs for early-career researchers, and joint research. They plan to begin by offering research internships through which undergraduate and graduate students can gain experience in laboratories at partner institutions. They will also expand opportunities for exchanges and collaboration among postdoctoral researchers and early-career faculty members.
The two sides will promote joint research in key scientific and technological fields, including artificial intelligence, quantum technology, advanced biotechnology, and advanced materials. They will also facilitate shared access to research infrastructure and organize regular workshops and symposia, enabling researchers to maintain sustained exchanges and develop new collaborative research projects.
The agreement strengthens “Global Connect,” KAIST’s strategy for international cooperation. Through this initiative, KAIST seeks to move beyond conventional university exchanges by closely integrating research, education, and talent mobility. It also aims to expand its global research network in partnership with Korea’s other science and technology-focused universities.
“Global cooperation must move beyond conventional exchanges and evolve into partnerships that combine complementary strengths to create new knowledge and cultivate talent together. By connecting the research capabilities of Université Paris-Saclay and K-STAR, we expect to generate new research achievements in critical fields such as AI, quantum technology, and biotechnology, while further strengthening Korea’s global network in science and technology,” said KAIST President Choongsik Bae.
The agreement is also significant because it expands the educational and research cooperation that KAIST has developed with France over the past 15 years to the K-STAR level.
KAIST also signed a student exchange agreement with CentraleSupélec, a grande école affiliated with Université Paris-Saclay, in 2011. The two institutions subsequently established a dual-degree program in mechanical engineering in 2017 and have continued to develop their partnership. Building on the experience and mutual trust accumulated through this cooperation, the new agreement broadens the partnership to encompass K-STAR and Université Paris-Saclay.
Université Paris-Saclay President Camille Galap said, “Université Paris-Saclay and K-STAR have broad research capabilities spanning basic science and advanced technology, giving this partnership considerable potential. I hope the agreement will promote more active exchanges among students and researchers from both countries and enable us to achieve new scientific advances through joint research.”
Jaemin Jung, KAIST Senior Vice President for Planning and Budget, said, “This agreement marks the beginning of our efforts to translate the university-level cooperation discussed at the ninth Korea-France Joint Committee on Science and Technology in April into concrete research collaboration. KAIST will support exchanges among students and researchers so that they can work in one another’s laboratories and produce tangible outcomes, including joint research projects, publications, and the shared use of research infrastructure.”
The signing ceremony was also attended by Stéphane Nonnenmacher, Director of the Graduate School of Mathematics at Université Paris-Saclay; Divya Madhavan, Director of International Relations at CentraleSupélec; and a delegation from the Korea Institute for Advanced Study (KIAS), a KAIST-affiliated institute.
2026.09.09 View 2072 -
KAIST Tames a Semiconductor Greenhouse Gas 6,000 Times More Potent Than CO₂ with the ‘Power of Disorder’
Among the gases used in semiconductor manufacturing, tetrafluoromethane (CF₄) is a greenhouse gas over 6,000 times more potent than carbon dioxide. A KAIST research team has developed a technology that removes this gas with high efficiency while extending the usable lifetime of the catalyst that helps break it down by harnessing the ‘power of disorder,’ in which mixing multiple metal atoms together actually stabilizes the catalyst’s structure.
KAIST (President Choongsik Bae) announced on September 3 that a research team led by Professor Minkee Choi from the Department of Chemical and Biomolecular Engineering, working in collaboration with researchers from Samsung Electronics, has developed a new catalyst capable of removing CF₄, a greenhouse gas used in processes such as the fabrication of fine semiconductor circuits with high efficiency over long periods of use.
CF₄ is used in processes such as dry etching, in which unwanted portions of a semiconductor wafer are selectively removed to create fine circuit patterns. The problem lies in the CF₄ left over after use. Because its carbon and fluorine atoms are bound together extremely tightly, the gas does not easily decompose, and once released into the atmosphere, it can persist for roughly 50,000 years. Its impact on global warming is also more than 6,000 times greater than that of carbon dioxide.
To prevent CF₄ from being released as is, semiconductor manufacturing sites currently decompose it at high temperatures using steam and a catalyst. A catalyst speeds up chemical reactions, much like those used to reduce pollutants in car exhaust.
However, conventional catalysts have suffered from declining performance the longer they are used. This is because hydrogen fluoride (HF), generated as CF₄ decomposes, combines with moisture to create a highly corrosive environment, causing the catalyst’s fine particles to aggregate or its structure to change. When small catalyst particles clump together into larger masses, the surface area in contact with the CF₄ to be treated shrinks, and performance declines accordingly.
The research team solved this problem, paradoxically, by harnessing the ‘power of disorder.’
Mixing multiple atom types creates a complex, disordered structure that resists phase changes and remains stable. This process is called entropy stabilization. In simple terms, it is a principle in which evenly mixing multiple kinds of atoms makes it difficult for a catalyst to clump together or change into another structure.
Using this principle, the research team evenly incorporated multiple metals — aluminum (Al), zinc (Zn), gallium (Ga), nickel (Ni), and cobalt (Co) — into a single aluminate crystal structure. Aluminate is a material in which several metals are bonded around a basic framework of aluminum and oxygen. Through this approach, the team developed an ‘entropy-stabilized aluminate (ESA) catalyst’ that resists aggregation and structural deformation even under the harsh conditions of high temperature, moisture, and fluorine occurring together.
The performance gap was clear. The new catalyst’s intrinsic activity for decomposing CF₄ was approximately 2.3 times higher than that of a conventional alumina catalyst. Notably, in an accelerated test conducted at about 800°C for 150 hours, the CF₄ conversion of the conventional alumina catalyst dropped from 93% to 48%. The new catalyst, by contrast, maintained a high level, declining only from 98% to 92%. This demonstrated that the catalyst can remove CF₄ with high efficiency while sustaining its performance over extended periods.
The researchers also revealed the decomposition mechanism of CF₄. To do this, they used oxygen isotopes, which allow the movement of oxygen atoms to be tracked. In simple terms, this involves attaching a ‘tag’ to oxygen atoms so that where the oxygen comes from and where it moves to during the reaction can be traced.
The results confirmed that the catalyst first uses the oxygen within its own structure to decompose CF₄, and that the reaction continues as surrounding steam replenishes the oxygen that has been depleted. In effect, the catalyst functions as a kind of ‘oxygen refill system,’ in which steam restores the oxygen the catalyst draws upon. Through this, the research team provided the world’s first experimental confirmation of a CF₄ decomposition process that had previously only been proposed in theory.
The significance of this research goes beyond developing a single catalyst that decomposes CF₄ effectively; it presents a new catalyst design strategy capable of achieving both high decomposition performance and a long service life at the same time. The approach is expected to be applicable to the future development of catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.
Professor Choi said, “By applying the principle that disorder in nature can actually make a structure more stable to catalyst design, we achieved both high CF₄ decomposition performance and long-term stability at the same time.” He added, “This work is meaningful in that it presents a new materials design strategy that can be extended to catalysts for treating a range of semiconductor process gases by varying the types and combinations of metals used.”
The study was led by Dr. Seunghyuck Chi, a postdoctoral researcher in KAIST’s Department of Chemical and Biomolecular Engineering, who served as first author, with researchers from Samsung Electronics participating as co-authors. The findings were published in June in the international chemistry journal Angewandte Chemie International Edition.
Paper title: Entropy-Stabilized Aluminate Catalysts that Break the Activity–Stability Tradeoff in CF₄ Hydrolysis,
DOI: 10.1002/anie.6752036
This research was supported by the National Research Foundation of Korea (RS‐2024‐00333937 and RS‐2024‐00405261).
2026.09.03 View 3037 -
KAIST Develops ‘Chameleon AI Semiconductor’ with Programmable Response Speeds
AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data changing at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots, and wearable devices.
KAIST (President Choongsik Bae) announced on August 7 that a research team led by Chair Professor Shinhyun Choi from the School of Electrical Engineering and the Graduate School of Semiconductor Technology has developed a programmable dynamic memtransistor (PDM), a semiconductor device whose time-response characteristics can be adjusted to multiple states and retained, as well as an integrated array based on the device.
A memtransistor is a next-generation semiconductor device that combines the information-storage function of memory with the computing function of a transistor. In the developed PDM, the ability to process data while retaining previous information allows its response characteristics to be adjusted and retained for incoming data.
Today’s computers and smartphones require complex software processing to analyze data that changes over time, resulting in large computational loads and high power consumption. To address this, researchers have been studying technologies that allow semiconductor hardware itself to process data directly. However, conventional devices have had fixed response speeds that cannot be changed once the device is fabricated.
The research team overcame this limitation by introducing a dual-layer structure inside the transistor, combining a charge storage layer that accumulates and processes data with an electron trapping layer that controls the response speed in a nonvolatile manner.
In the PDM developed by the research team, incoming data is processed in the charge storage layer, while the electron trapping layer controls, across multiple levels, the recovery speed at which the semiconductor returns to its original state. In experiments, the team succeeded in tuning the current recovery time over an approximately 5-fold range and the characteristic frequency over a range of more than 10-fold.
In particular, in experiments involving the prediction of data in which fast and slow changes are intricately mixed, the PDM reduced prediction errors by as much as 40 times compared with conventional fixed-response semiconductor devices. The PDM enables accurate information processing even when handwriting or object-movement speeds vary, by using response characteristics configured to match different input timescales. Once the response characteristics are set, the device remembers them without requiring a continuous external power supply, and it does not require complex preprocessing of input data. Because it is fully compatible with materials used in widely adopted commercial semiconductor processes, it is also highly advantageous for mass production and commercialization.
The research team fabricated a PDM array and used it to predict complex data, confirming that it achieved accuracy comparable to conventional software-based systems while consuming far less energy.
“This study demonstrates an AI semiconductor whose response characteristics can be programmed to efficiently process data changing at different speeds,” said Chair Professor Choi. “We expect it to become a core technology that improves the performance of AI devices such as autonomous vehicles, robots, and wearables while reducing their power consumption.”
This research was led by KAIST Graduate School of Semiconductor Technology Ph.D. candidate Dae-won Kim as the first author, with Yoonho Cho, Seokho Seo, Yujin Kim, See-On Park, Taehwan Jang, and Chaebin Park participating as co-authors. Young Taek Oh and Fellow Jae-Duk Lee of Samsung Electronics’ Semiconductor R&D Center also participated as co-authors, and Chair Professor Shinhyun Choi served as the corresponding author. The research was published in July in the internationally renowned journal Nature Communications on July 4.
Paper title: Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing,
DOI: https://doi.org/10.1038/s41467-026-75211-5
This research was supported by the National R&D Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT, the ETRI R&D Support Program of the Institute of Information & Communications Technology Planning & Evaluation, the HRD Program for Industrial Innovation of the Korea Institute for Advancement of Technology funded by the Ministry of Trade, Industry and Energy, Samsung Electronics, and others.
2026.08.07 View 4194 -
KAIST and Seoul National University Students Hold 100-Hour Robot Hackathon to Nurture Physical AI Talent
KAIST (President Choongsik Bae) announced on August 4 that RoboticUS, a joint student organization formed by students from KAIST and Seoul National University, is holding the inaugural Robot Hackathon at KAIST from August 3 to 8.
"In the era of Physical AI, we need convergence talent who can go beyond building good AI to design and implement robots and systems that move the real world based on AI," said Choongsik Bae, President of KAIST. "This hackathon, planned and run entirely by students, is a good example of KAIST's culture of challenge and collaboration, and we expect it to become a new educational model for turning future technologies into reality," he added.
The hackathon puts this educational philosophy directly into students' hands. It is Korea's first student-led Physical AI robot hackathon, planned and run by students from KAIST and Seoul National University across institutional boundaries. Participants experience the entire process of designing and building working robots, developing hands-on capabilities that integrate AI and hardware.
The event is hosted by RoboticUS, a nonprofit student organization formed jointly by MR, a robotics club in KAIST's Department of Mechanical Engineering, and Seoul National University's robotics clubs SHAPE and SIGMA. Students who share a passion for robotics from the two universities joined forces across institutional lines, handling every stage themselves — from recruiting participants to designing the mission, running the event, and setting up the presentation and judging format. KAIST's Department of Mechanical Engineering supports the event with facilities and operational assistance so that the students' initiative can translate into genuine educational value.
Ten teams — 30 students total — selected from the two universities will take part. After receiving training in power circuits and robot joint control on August 3 and 4, participants will begin building their robots when the mission is unveiled on the morning of August 5 and continue working until 4 p.m. on August 8. Starting from an idea, they will go through design, assembly, programming, and repeated testing to complete a working robot — experiencing the full roughly 100-hour cycle themselves.
Each team will be provided with Angel Robotics' "phact" actuator, which serves as the robot's joints and muscles, and NVIDIA's Jetson AGX, which functions as the robot's brain. Taejin Technology Co., Ltd will provide training on circuits and electronic components for supplying stable power to the robots, and Angel Robotics will support hands-on training in using the actuators and controlling the robots.
Participants will not simply assemble a finished kit — they will design the robot's shape and movement from the ground up and build it themselves.
For fairness, the mission will be revealed only at the start of the hackathon on August 5. There is no single correct answer or predetermined robot form. Each team will interpret the same mission differently, combining mechanical structure, circuitry, AI, and control software into a single robot. One of the highlights will be seeing the different solutions the ten teams develop in response to the same mission.
The final day, August 8, will be an open Physical AI festival that welcomes the general public. A public conference at the KI Building (E4) Fusion Hall will introduce the current state of Physical AI in an accessible and engaging way — from robotic skin that lets robots feel touch like humans, to humanoid robots that can see and hear people, to a quadrupedal robot that has completed a marathon.
Professors Jung Kim, Yong-Hwa Park, and Jemin Hwangbo of the Department of Mechanical Engineering, along with Joon-Ha Kim, CEO of Diden Robotics, will each give a talk on robotic skin and haptics, multimodal perception in humanoid robots, the quadrupedal robot Raibo, and the journey of developing Physical AI for industrial use, respectively.
After the conference, an open demo day for the ten participating teams will run from 4 to 5 p.m. at KAIST's Culture Complex (E9), 3rd floor. Members of the public will be able to visit each team's booth, watch the robots the students built over 100 hours in action, and submit their own evaluations via QR code.
Judging criteria include mission achievement, technical execution, creativity, and presentation and demonstration. The final score will weight faculty advisor evaluation at 30%, peer evaluation among hackathon participants at 30%, sponsor judging panel evaluation at 30%, and pre-registered public attendee evaluation at 10%.
Angel Robotics and Taejin Technology are taking part as core technology partners, providing equipment and training. Faculty members and industry experts are providing education and technical guidance so that students can safely handle equipment used in real research and industrial settings.
"Physical AI's competitiveness comes not just from software but from hardware and control technology," said Kyoungchul Kong, Professor from Mechanical Engineering at KAIST and Head of the Future Technology Institute at Angel Robotics. "This hackathon will show that with high-performance robot components and the right development environment in place, even undergraduates can turn their imagination into a working robot in 100 hours," he added.
"This hackathon is about learning and building together, rather than competing between schools," said Yeonsu An, President of RoboticUS (and President of MR, KAIST's robotics club). "We hope the general public will get to experience the robots students have built firsthand and take part in the judging, coming away with the sense that Physical AI is a technology anyone can understand and enjoy — not just something for experts," she added.
“Although we do not yet know what the challenge will be, I am most looking forward to gathering in one place and developing robots together,” said Hyeontae Jeon, a participating student from Seoul National University. “It will be even more meaningful to work through challenges across university boundaries and present the robots we built ourselves to the public.”
The public conference is open to anyone through pre-registration. Pre-registered attendees can watch the lectures, view the open demo day, and take part in on-site judging. Registration is available on the RoboticUS official website or on Event-us, under "First Robot Hackathon – Robot & AI Public Conference (KAIST × SNU)." Registration closes August 6.
2026.08.04 View 4536 -
KAIST Develops AI That Finds Its Own Hidden Weaknesses, Paving the Way for Safer Generative AI Models
KAIST researchers have developed a safety verification technology that uncovers roughly seven times more hidden vulnerabilities in AI than existing methods. The technology is expected to serve as a foundation for developing safer, more trustworthy AI.
KAIST (President Choongsik Bae) announced on the 30th of July that a research team led by Professor Junmo Kim from the School of Electrical Engineering has developed a new framework called Stable-GFlowNet (S-GFN), which overcomes the limitations of red-teaming—a safety verification process that deliberately attacks large language models (LLMs) to expose hidden weaknesses.
Red-teaming for generative AI is the process of crafting attack prompts designed to probe an AI's vulnerabilities and induce the AI to produce harmful or dangerous responses before the program is deployed. Since discovering a wider variety of attack methods allows more vulnerabilities to be addressed in advance, both the success rate and diversity of attacks are critical.
Previous approaches primarily relied on reinforcement learning—an AI technique trained to maximize reward—to generate attack prompts. However, these methods frequently suffered from mode collapse—a phenomenon where the model repeatedly converges on a narrow set of high-reward attack prompts rather than generating diverse outputs, thereby limiting its ability to uncover various vulnerabilities.
Generative Flow Networks (GFlowNets)—an AI generation technique trained to produce diverse outputs in proportion to their reward—were proposed as a solution. Yet GFlowNet training is computationally complex and unstable, and noisy reward signals can assign high rewards even to meaningless sentences, often causing training to collapse.
To address these issues, the research team developed three core techniques that help the model learn effective attacks more reliably while filtering out flawed ones.
First, much like comparing several paths to choose the best one, the team introduced Contrastive Trajectory Balance (CTB), which reduces computational complexity and stabilizes training by directly comparing pairs of generated attack trajectories.
Second, akin to filtering out background noise to focus on a single voice, the team applied Noise Gradient Pruning (NGP) to eliminate minor reward fluctuations and ensure the model learns exclusively from meaningful signals.
Third, the team applied the Min-K Fluency Stabilizer (MKS), which guides the model to generate attack prompts resembling text that a real user would write—just as a human reader naturally prefers coherent sentences to gibberish.
As a result, Stable-GFlowNet discovered 134 unique attack types—about seven times more than the 17 unique attack types found by the existing GFlowNet-based technique—while maintaining a high attack success rate of 92%.
Defense models trained using attacks generated by Stable-GFlowNet also demonstrated strong generalization, effectively defending against a wide range of attacks in cross-attack tests, which evaluate performance using attack techniques different from those used during training.
The team further demonstrated that CTB and NGP achieve faster and more stable performance than existing methods—not only in AI safety verification, but also in other distribution-matching tasks such as molecular generation for drug discovery.
Professor Kim said, "This technology is significant in that it can reliably uncover a wide range of AI vulnerabilities even in realistic conditions with limited data and high noise." He added, "Because it allows a broader range of risk factors to be identified and defended against before generative AI is deployed in real-world services, we expect it to become a core foundational technology for developing safer, more trustworthy AI."
The study was led by first author Minchan Kwon, a Ph.D. candidate from the School of Electrical Engineering, and was selected as a Spotlight paper—placing it in the top 2.2% of submissions—at the International Conference on Machine Learning (ICML) 2026, one of the world's most prestigious AI conferences.
※ Paper title: Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
arXiv: https://arxiv.org/abs/2605.00553
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation’s (IITP) SW Star Lab program, funded by the Ministry of Science and ICT.
2026.07.30 View 3140 -
KAIST and NVIDIA Launch Human Physical AI NVAITC
A new era of Physical AI is taking shape, enabling wearable robots and humanoids to understand and predict human movement and achieve more precise control. KAIST, which possesses world-class research capabilities in wearable robotics, and NVIDIA will collaborate to develop a Human Motion Foundation Model that enables AI to learn human movement and physical intelligence.
KAIST, led by President Choongsik Bae, announced on July 25 that it will establish a NVIDIA AI Technology Center (NVAITC) with NVIDIA to advance collaborative research in Physical AI.
As the Korean government advances Physical AI as a key national initiative for the country’s future, the collaboration aims to secure core technologies for next-generation Physical AI by combining KAIST’s human-centered robotics technologies and real-world human motion data with NVIDIA AI technologies and global research network.
The collaboration will be carried out through the establishment of the Human Physical AI NVAITC by the KAIST Department of Mechanical Engineering and NVIDIA. The Human Physical AI Research Center at the KAIST Department of Mechanical Engineering will serve as the core research hub for the NVAITC . Building on this foundation, the two organizations plan to progressively expand the scope of their collaboration across the full spectrum of Physical AI, including wearable robots, humanoids, digital twins, and manufacturing.
“Competitiveness in the era of Physical AI will depend not simply on AI itself, but on domain-specific technologies and data grounded in a deep understanding of humans and robots,” said KAIST President Choongsik Bae. “By combining KAIST’s accumulated expertise in human-centered research with NVIDIA’s world-leading AI infrastructure and physical AI technologies, we will realize Physical AI that better understands and supports people and develop KAIST into a global hub leading Physical AI research and industry beyond Korea.”
The Human Physical AI Research Center was established around the laboratories of Professor Kyoungchul Kong, a leading researcher in wearable robotics, and Professor Jung Kim, a leading researcher in biorobotics. Professors Kim and Kong serve as co-directors of the Center.
The Center conducts research to understand how humans move, exert force, and maintain balance in real-world environments and to reproduce these capabilities through AI and robotics. In particular, the large-scale human motion data and gait and movement control technologies accumulated through wearable robotics research are regarded as a critical foundation for developing human-centered Physical AI.
Co-director Professor Kyoungchul Kong is a world-renowned researcher in wearable robotics who has developed robotic technologies for gait assistance and rehabilitation. Through Angel Robotics, a company he founded, he has also led the commercialization of wearable robotics by translating research outcomes into real-world products and services. Through the NVAITC , Professor Kong will lead the development of the Human Motion Foundation Model based on the human motion data and robotic control technologies accumulated by his research team.
On NVIDIA’s side, Charles Cheung, Senior Manager at the NVIDIA AI Technology Center (NVAITC), will participate by providing expert technical consultation and developer support. The NVAITC will also operate research and educational programs using NVIDIA Omniverse and digital twin platforms.
“The KAIST Human Physical AI Research Center has world-class human motion data and research capabilities in wearable robotics,” said Charles Cheung. “This research, which seeks to reproduce human movement through AI, is expected to open new possibilities for Physical AI.”
To ensure the systematic operation of the collaborative research, the two organizations will establish a Steering Committee and review research goals and progress every six months. They also plan to hold an annual international symposium that will bring together researchers from Korea and abroad to share the latest research outcomes and industry trends in Physical AI.
A Student Ambassador Program will also be offered to KAIST students. Through the program, NVIDIA experts will provide lectures and regular office hours and carry out projects with participating students.
The first cohort is expected to consist of five to 10 students. Participants will receive training focused on NVIDIA Omniverse and digital twin technologies and will be awarded certificates upon completion of the program.
The primary objective of the first phase of the collaborative research is to develop a Human Motion Foundation Model.
The Human Motion Foundation Model is a generative AI-based model trained on large-scale human motion data to understand, predict, and generate a wide range of human movements. It is expected to serve as a core enabling technology that will allow wearable robots and humanoids to more accurately identify users’ intentions and movements and respond more naturally.
The technologies developed through the NVAITC are expected to be applied not only to wearable robots that support the rehabilitation and daily lives of people with gait impairments, but also to humanoids that work alongside humans, human movement assessment, and digital healthcare.
Ultimately, the researchers aim to explain from an AI perspective how humans plan movement and control their muscles and joints. Based on this understanding, they seek to create next-generation robotic systems that help people overcome gait impairments and expand human physical capabilities.
The collaboration is also significant because its impact is expected to extend beyond an individual research project and contribute to the broader Physical AI industrial ecosystem in Korea.
Co-directors Professors Jung Kim and Kyoungchul Kong are currently leading in a Deep Tech Scale-up Valley project in the field of Physical AI. By combining Angel Robotics’ experience in technology commercialization, KAIST’s capabilities in robotics, mechanical engineering, and AI, and NVIDIA’s AI technologies and global professional network, the collaboration is expected to support a broad range of activities spanning research and development, talent cultivation, startup support, and technology commercialization.
“This collaboration will provide an important opportunity to take AI research in the Department of Mechanical Engineering to the next level,” said Professor Hyung-Soon Park, Head of the KAIST Department of Mechanical Engineering. “Centered around the Human Physical AI Research Center, we will expand Physical AI research into nationally strategic industries, including robotics and manufacturing.”
2026.07.26 View 6084 -
KAIST Makes Buttons Rise with Light
No wires. No actuators. Shine light on the metal surface, and it rises like a button. KAIST researchers have developed a metal structure that changes shape using light, without any light-absorbing coating. This technology could open new possibilities for tactile interfaces with physical pop-up buttons, shape displays, next-generation wearable devices, and soft robots.
KAIST (President Choongsik Bae) announced on the 20th of July that a research team led by Professor Il-Kwon Oh from the Department of Mechanical Engineering has developed a technology that transforms a flat NiTi shape-memory alloy (SMA) sheet into a “photothermally driven meta-morphing structure” that rises from a flat surface into a three-dimensional form when exposed to light, using only a single UV-laser process.
Next-generation wearable devices and soft robots require technologies that are thin and lightweight while also being capable of changing into desired shapes when needed. Such technologies are attracting attention as a foundation for shape displays, adaptive surfaces, wearable interfaces, and soft robots.
The research team designed precise cutting and folding patterns in a flat metal sheet so that it would transform into a predetermined three-dimensional shape. The design principle is based on kirigami, the art of creating three-dimensional structures by cutting paper.
Shape-memory alloys are special metals that return to a pre-programmed shape when heated to a specific temperature, even after being deformed. Because they are lightweight and can generate large forces, they are widely used as key materials for soft robots and wearable actuators. However, conventional photothermal shape-memory alloy actuators have faced a limitation: nickel-titanium alloy (NiTi) surfaces do not absorb near-infrared light efficiently. To compensate for this, separate light-absorbing coatings such as graphene oxide, polymer composites, or titanium nitride (TiN) thin films have typically been applied.
These external coatings can peel off during repeated operation and require additional processing. They can also increase heat capacity, which may slow the response, creating limitations in both manufacturability and actuation performance.
To address this problem, the research team used UV laser micromachining. Through this process, they formed kirigami structures on thin shape-memory alloy (SMA) sheets while simultaneously generating a micro-nano porous titanium oxide (TiOₓ) layer on the surface through laser-induced oxidation. As a result, they were able to significantly increase the absorption of near-infrared light without any separate external coating.
The team also implemented a platform that can precisely control the height of three-dimensional deformation and the resulting force output by adjusting structural parameters such as hinge width and slit width. In other words, the core of this research lies in simultaneously programming both how the structure mechanically deforms and how efficiently it absorbs light within a single metal structure.
Furthermore, the team applied a patterning technique that spatially controls the degree of laser-induced oxidation. This made it possible for different regions to deform sequentially at different speeds, even when exposed uniformly to light of the same intensity. The researchers describe this as “spatiotemporal actuation control.” This means that the order and timing of deformation are encoded directly into the material itself through light-absorption properties, without any separate electrical control. It can be seen as a form of photonic logic.
The research team further expanded the photothermal SMA metastructure into three-dimensional shape displays and haptic interfaces by integrating it with a multi-channel near-infrared (NIR) LED array. Each SMA kirigami unit moves independently in response to selectively applied light. Based on this, the team successfully displayed the letter sequence K→A→I→S→T and implemented tactile navigation signals that indicate direction.
Professor Il-Kwon Oh said, “The laser programming technology developed in this study is a manufacturing-friendly platform that encodes both mechanical deformation and optical properties into a single metallic structure without any separate coating process,” adding, “It can be widely applied to next-generation intelligent morphing interfaces controlled by light, including adaptive surfaces, interactive haptics, and photothermal soft robots.”
Hyunsoo Kim, a master’s student in the Department of Mechanical Engineering, served as the first author, while Professor Il-Kwon Oh was the corresponding author. The results were published in the international journal Advanced Science, and the work was also selected for the Inside Back Cover of Advanced Science, Vol. 13, No. 31, published on June 4, 2026.
Paper title: Monolithic UV-Laser Programming of Photothermally Meta-Morphing SMA Structures: Dual-Encoded Kirigami Mechanics and Photonic Absorbance
DOI: https://doi.org/10.1002/advs.74930
Related Video: https://drive.google.com/drive/folders/16Q9C1D6EMjoM2ypNmtDGU9ruMBdZeUFs
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00345241 and RS-2023-00302525). This research was supported by the Nano & Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2025-25441263). This research was supported by the InnoCORE program of the Ministry of Science and ICT (N10250154).
2026.07.20 View 3738 -
KAIST Develops Robot That Judges Its Surroundings and Walks, Runs, and Jumps Like an Animal
An era in which robots decide "how to walk" on their own has arrived. A four-legged robot has been developed that, much like a person or an animal, autonomously chooses the appropriate gait strategy for its surroundings — changing its gait on stairs, leaping over gaps, and keeping its balance on forest trails.
KAIST (President Choongsik Bae) announced on the 16th of July that a research team led by Professor Hae-Won Park from the Department of Mechanical Engineering has developed a core control technology for four-legged robots that lets a single controller select and switch in real time among walking, running, jumping, and other locomotion skills, allowing the robot to move quickly and stably, even in real outdoor environments.
Four-legged robots move on four legs, giving them an advantage over wheeled robots on rough terrain. But in real outdoor settings, obstacles such as stairs, ledges, stepping stones, gaps, and tree branches appear one after another in different forms, meaning the ability to simply walk and run fast is not enough.
Existing four-legged robots have excelled at running quickly across flat ground or clearing simple obstacles, but they have struggled to maintain both speed and stability in real-world environments where obstacles combine in complex ways. Because walking, running, jumping, and other gaits had to be controlled individually, the robots were also limited in how naturally they could switch between them as conditions changed.
To overcome these limitations, the research team developed a new learning-based control technology called APT-RL (Action Pretrained Transformer-based Reinforcement Learning).
APT-RL is a control technology designed to enable a robot to first learn a range of locomotion skills — such as walking, running, and jumping — and then freely combine and transition among them in real-world environments as the situation demands.
Rather than filming the movements of real people or animals, the team generated 15.5 hours of training data covering a variety of gaits using computer simulations alone, in just eight minutes. That data was used to teach the robot basic movement capabilities, drawing on robot dynamics (a mathematical model of how a robot moves) and trajectory optimization (a technique for calculating the efficient path of movement). The approach is far faster and more efficient than earlier methods that relied on motion capture, a technology that records human or animal movement using sensors.
The team then applied reinforcement learning — an artificial intelligence technique in which an agent learns optimal behavior through repeated trial and error — so the robot could autonomously select and switch gaits suited to complex three-dimensional terrain such as stairs, ledges, and gaps. Finally, the team combined a depth camera (which measures the distance to objects in order to obtain three-dimensional information) with LiDAR (Laser Detection and Ranging, a sensor that uses lasers to measure the distance and shape of the surrounding environment in three dimensions), enabling the robot to recognize its surroundings and target speed in real time and choose the most appropriate walking strategy.
The team tested the control technology on its own four-legged robot, 'KAIST HOUND.' The experiments were conducted not only on an indoor obstacle course but also in real outdoor environments, including KAIST’s campus and forest trails.
KAIST HOUND moved stably across urban terrain that included stairs, grass, and slopes, as well as irregular natural terrain such as fallen trees, exposed roots, and paths covered in fallen leaves, switching gaits in real time to match the conditions. In rugged terrain with obstacles, the robot reached a peak instantaneous speed of six meters per second (about 22 kilometers per hour), demonstrating that it can achieve both fast movement and stability in real outdoor environments.
The experiments showed that KAIST HOUND autonomously selected and switched between a trot (alternating diagonal legs) and a bound (a leaping gait using the front and back leg pairs together) depending on the terrain and target speed, and that it could integrate walking, running, jumping, and ledge-clearing into a single controller.
Professor Hae-Won Park said "We expect this to become a foundational technology that expands the potential uses of physical-AI-based walking robots in rugged environments such as disaster sites, defense missions, and industrial facility inspections."
Jun-Gill Kang (affiliated with the Agency for Defense Development (ADD) at the time of the research) and Jaehyun Park, a Ph.D. candidate in KAIST's Department of Mechanical Engineering, are co-first authors of the study. Professor Hae-Won Park and Professor Seungwoo Hong from Korea University are co-corresponding authors. The research was selected as the cover paper for the July issue of Science Robotics, the world's leading academic journal in robotics, and was published on July 15 (U.S. Eastern time).
Paper title: Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
DOI: 10.1126/scirobotics.adz7397
Authors: Jun-Gill Kang (the Agency for Defense Development at the time of the research, co-first author), Jaehyun Park (KAIST, co-first author), Hae-Won Park (KAIST, corresponding author), Seungwoo Hong (Korea University, corresponding author)
This research was supported by funding from the Ministry of Trade, Industry and Resources (MOTIR) and the Korea Planning & Evaluation of Industrial Technology (KEIT) (RS-2024-00427719), as well as by the Agency for Defense Development's Future Challenge Defense Technology R&D program (912768601).
2026.07.16 View 5524