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KAIST debuts “DreamWaQer” - a quadrupedal robot that can walk in the dark
- The team led by Professor Hyun Myung of the School of Electrical Engineering developed “DreamWaQ”, a deep reinforcement learning-based walking robot control technology that can walk in an atypical environment without visual and/or tactile information - Utilization of “DreamWaQ” technology can enable mass production of various types of “DreamWaQers” - Expected to be used in exploration of atypical environment involving unique circumstances such as disasters by fire. A team of Korean engineering researchers has developed a quadrupedal robot technology that can climb up and down the steps and moves without falling over in uneven environments such as tree roots without the help of visual or tactile sensors even in disastrous situations in which visual confirmation is impeded due to darkness or thick smoke from the flames. KAIST (President Kwang Hyung Lee) announced on the 29th of March that Professor Hyun Myung's research team at the Urban Robotics Lab in the School of Electrical Engineering developed a walking robot control technology that enables robust 'blind locomotion' in various atypical environments. < (From left) Prof. Hyun Myung, Doctoral Candidates I Made Aswin Nahrendra, Byeongho Yu, and Minho Oh. In the foreground is the DreamWaQer, a quadrupedal robot equipped with DreamWaQ technology. > The KAIST research team developed "DreamWaQ" technology, which was named so as it enables walking robots to move about even in the dark, just as a person can walk without visual help fresh out of bed and going to the bathroom in the dark. With this technology installed atop any legged robots, it will be possible to create various types of "DreamWaQers". Existing walking robot controllers are based on kinematics and/or dynamics models. This is expressed as a model-based control method. In particular, on atypical environments like the open, uneven fields, it is necessary to obtain the feature information of the terrain more quickly in order to maintain stability as it walks. However, it has been shown to depend heavily on the cognitive ability to survey the surrounding environment. In contrast, the controller developed by Professor Hyun Myung's research team based on deep reinforcement learning (RL) methods can quickly calculate appropriate control commands for each motor of the walking robot through data of various environments obtained from the simulator. Whereas the existing controllers that learned from simulations required a separate re-orchestration to make it work with an actual robot, this controller developed by the research team is expected to be easily applied to various walking robots because it does not require an additional tuning process. DreamWaQ, the controller developed by the research team, is largely composed of a context estimation network that estimates the ground and robot information and a policy network that computes control commands. The context-aided estimator network estimates the ground information implicitly and the robot’s status explicitly through inertial information and joint information. This information is fed into the policy network to be used to generate optimal control commands. Both networks are learned together in the simulation. While the context-aided estimator network is learned through supervised learning, the policy network is learned through an actor-critic architecture, a deep RL methodology. The actor network can only implicitly infer surrounding terrain information. In the simulation, the surrounding terrain information is known, and the critic, or the value network, that has the exact terrain information evaluates the policy of the actor network. This whole learning process takes only about an hour in a GPU-enabled PC, and the actual robot is equipped with only the network of learned actors. Without looking at the surrounding terrain, it goes through the process of imagining which environment is similar to one of the various environments learned in the simulation using only the inertial sensor (IMU) inside the robot and the measurement of joint angles. If it suddenly encounters an offset, such as a staircase, it will not know until its foot touches the step, but it will quickly draw up terrain information the moment its foot touches the surface. Then the control command suitable for the estimated terrain information is transmitted to each motor, enabling rapidly adapted walking. The DreamWaQer robot walked not only in the laboratory environment, but also in an outdoor environment around the campus with many curbs and speed bumps, and over a field with many tree roots and gravel, demonstrating its abilities by overcoming a staircase with a difference of a height that is two-thirds of its body. In addition, regardless of the environment, the research team confirmed that it was capable of stable walking ranging from a slow speed of 0.3 m/s to a rather fast speed of 1.0 m/s. The results of this study were produced by a student in doctorate course, I Made Aswin Nahrendra, as the first author, and his colleague Byeongho Yu as a co-author. It has been accepted to be presented at the upcoming IEEE International Conference on Robotics and Automation (ICRA) scheduled to be held in London at the end of May. (Paper title: DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning) The videos of the walking robot DreamWaQer equipped with the developed DreamWaQ can be found at the address below. Main Introduction: https://youtu.be/JC1_bnTxPiQ Experiment Sketches: https://youtu.be/mhUUZVbeDA0 Meanwhile, this research was carried out with the support from the Robot Industry Core Technology Development Program of the Ministry of Trade, Industry and Energy (MOTIE). (Task title: Development of Mobile Intelligence SW for Autonomous Navigation of Legged Robots in Dynamic and Atypical Environments for Real Application) < Figure 1. Overview of DreamWaQ, a controller developed by this research team. This network consists of an estimator network that learns implicit and explicit estimates together, a policy network that acts as a controller, and a value network that provides guides to the policies during training. When implemented in a real robot, only the estimator and policy network are used. Both networks run in less than 1 ms on the robot's on-board computer. > < Figure 2. Since the estimator can implicitly estimate the ground information as the foot touches the surface, it is possible to adapt quickly to rapidly changing ground conditions. > < Figure 3. Results showing that even a small walking robot was able to overcome steps with height differences of about 20cm. >
Professor Sang Kil Cha Receives IEEE Test-of-Time Award
Professor Sang Kil Cha from the Graduate School of Information Security (GSIS) in the School of Computing received the Test-of-Time Award from IEEE Security & Privacy, a top conference in the field of information security. The Test-of-Time Award recognizes the research papers that have influenced the field of information security the most over the past decade. Three papers were selected this year, and Professor Cha is the first Korean winner of the award. The paper by Professor Cha was published in 2012 under the title, “Unleashing Mayhem on Binary Code”. It was the first to ever suggest an algorithm that automatically finds bugs in binary code and creates exploits that links them to an attack code. The developed algorithm is a core technique used for world-class cyber security hacking competitions like the Cyber Grand Challenge, an AI hacking contest. Starting with this research, Professor Cha has carried out various studies to develop technologies that can find bugs and vulnerabilities through binary analyses, and is currently developing B2R2, a Korean platform that can analyze various binary codes.
Professor Hyunjoo Jenny Lee to Co-Chair IEEE MEMS 2025
Professor Hyunjoo Jenny Lee from the School of Electrical Engineering has been appointed General Chair of the 38th IEEE MEMS 2025 (International Conference on Micro Electro Mechanical Systems). Professor Lee, who is 40, is the conference’s youngest General Chair to date and will work jointly with Professor Sheng-Shian Li of Taiwan’s National Tsing Hua University as co-chairs in 2025. IEEE MEMS is a top-tier international conference on microelectromechanical systems and it serves as a core academic showcase for MEMS research and technology in areas such as microsensors and actuators. With over 800 MEMS paper submissions each year, the conference only accepts and publishes about 250 of them after a rigorous review process recognized for its world-class prestige. Of all the submissions, fewer than 10% are chosen for oral presentations.
Prof. Changho Suh Named the 2021 James L. Massey Awardee
Professor Changho Suh from the School of Electrical Engineering was named the recipient of the 2021 James L.Massey Award. The award recognizes outstanding achievement in research and teaching by young scholars in the information theory community. The award is named in honor of James L. Massey, who was an internationally acclaimed pioneer in digital communications and revered teacher and mentor to communications engineers. Professor Suh is a recipient of numerous awards, including the 2021 James L. Massey Research & Teaching Award for Young Scholars from the IEEE Information Theory Society, the 2019 AFOSR Grant, the 2019 Google Education Grant, the 2018 IEIE/IEEE Joint Award, the 2015 IEIE Haedong Young Engineer Award, the 2013 IEEE Communications Society Stephen O. Rice Prize, the 2011 David J. Sakrison Memorial Prize (the best dissertation award in UC Berkeley EECS), the 2009 IEEE ISIT Best Student Paper Award, the 2020 LINKGENESIS Best Teacher Award (the campus-wide Grand Prize in Teaching), and the four Departmental Teaching Awards (2013, 2019, 2020, 2021). Dr. Suh is an IEEE Information Theory Society Distinguished Lecturer, the General Chair of the Inaugural IEEE East Asian School of Information Theory, and a Member of the Young Korean Academy of Science and Technology. He is also an Associate Editor of Machine Learning for the IEEE Transactions on Information Theory, the Editor for the IEEE Information Theory Newsletter, a Column Editor for IEEE BITS the Information Theory Magazine, an Area Chair of NeurIPS 2021, and on the Senior Program Committee of IJCAI 2019–2021.
Prof. Junil Choi Receives the Neal Shepherd Memorial Award
Professor Junil Choi of the School of Electrical Engineering received the 2021 Neal Shepherd Memorial Award from the IEEE Vehicular Technology Society. The award recognizes the most outstanding paper relating to radio propagation published in major journals over the previous five years. Professor Cho, the recipient of the 2015 IEEE Signal Processing Society’s and the 2019 IEEE Communications Society’s Best Paper Award, was selected as the awardee for his paper titled “The Impact of Beamwidth on Temporal Channel Variation in Vehicular Channels and Its Implications” in IEEE Transaction on Vehicular Technology in 2017. In this paper, Professor Choi and his team derived the channel coherence time for a wireless channel as a function of the beamwidth, taking both Doppler effect and pointing error into consideration. The results showed that a nonzero optimal beamwidth exists that maximizes the channel coherence time. To reduce the impact of the overhead of doing realignment in every channel coherence time, the paper showed that the beams should be realigned every beam coherence time for the best performance. Professor Choi said, “It is quite an honor to receive this prestigious award following Professor Joonhyun Kang who won the IEEE VTS’s Jack Neubauer Memorial Award this year. It shows that our university’s pursuit of excellence in advanced research is being well recognized.”
Professor Kang’s Team Receives the IEEE Jack Newbauer Memorial Award
Professor Joonhyuk Kang of the School of Electrical Engineering received the IEEE Vehicular Technology Society’s 2021 Jack Neubauer Memorial Award for his team’s paper published in IEEE Transactions on Vehicular Technology. The Jack Neubauer Memorial Award recognizes the best paper published in the IEEE Transactions on Vehicular Technology journal in the last five years. The team of authors, Professor Kang, Professor Sung-Ah Chung at Kyungpook National University, and Professor Osvaldo Simeone of King's College London reported their research titled Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning in IEEE Transactions on Vehicular Technology, Vol. 67, No. 3, pp. 2049-2063, in March 2018. Their paper shows how the trajectory of aircraft is optimized and resources are allocated when unmanned aerial vehicles perform edge computing to help mobile device calculations. This paper has currently recorded nearly 400 citations (based on Google Scholar). "We are very happy to see the results of proposing edge computing using unmanned aerial vehicles by applying optimization theory, and conducting research on trajectory and resource utilization of unmanned aerial vehicles that minimize power consumption," said Professor Kang.
Professor Jaehyouk Choi, IT Young Engineer of the Year
Professor Jaehyouk Choi from the KAIST School of Electrical Engineering won the ‘IT Young Engineer Award’ for 2020. The award was co-presented by the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Electronics Engineers of Korea (IEIE), and sponsored by the Haedong Science and Culture Foundation. The ‘IT Young Engineer Award’ selects only one mid-career scientist or engineer 40 years old or younger every year, who has made a great contribution to academic or technological advancements in the field of IT. Professor Choi’s research topics include high-performance semiconductor circuit design for ultrahigh-speed communication systems including 5G communication. In particular, he is widely known for his field of the ‘ultra-low-noise, high-frequency signal generation circuit,’ key technology for next-generation wired and wireless communications, as well as for memory systems. He has published 64 papers in SCI journals and at international conferences, and applied for and registered 25 domestic and international patents. Professor Choi is also an active member of the Technical Program Committee of international symposiums in the field of semiconductor circuits including the International Solid-State Circuits Conference (ISSCC) and the European Solid-State Circuit Conference (ESSCIRC). Beginning this year, he also serves as a distinguished lecturer at the IEEE Solid-State Circuit Society (SSCS). (END)
Professor Jee-Hwan Ryu Receives IEEE ICRA 2020 Outstanding Reviewer Award
Professor Jee-Hwan Ryu from the Department of Civil and Environmental Engineering was selected as this year’s winner of the Outstanding Reviewer Award presented by the Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation (IEEE ICRA). The award ceremony took place on June 5 during the conference that is being held online May 31 through August 31 for three months. The IEEE ICRA Outstanding Reviewer Award is given every year to the top reviewers who have provided constructive and high-quality thesis reviews, and contributed to improving the quality of papers published as results of the conference. Professor Ryu was one of the four winners of this year’s award. He was selected from 9,425 candidates, which was approximately three times bigger than the candidate pool in previous years. He was strongly recommended by the editorial committee of the conference. (END)
Professor Jong Chul Ye Appointed as Distinguished Lecturer of IEEE EMBS
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was appointed as a distinguished lecturer by the International Association of Electrical and Electronic Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS). Professor Ye was invited to deliver a lecture on his leading research on artificial intelligence (AI) technology in medical video restoration. He will serve a term of two years beginning in 2020. IEEE EMBS's distinguished lecturer program is designed to educate researchers around the world on the latest trends and technology in biomedical engineering. Sponsored by IEEE, its members can attend lectures on the distinguished professor's research subject. Professor Ye said, "We are at a time where the importance of AI in medical imaging is increasing.” He added, “I am proud to be appointed as a distinguished lecturer of the IEEE EMBS in recognition of my contributions to this field.” (END)
Professor Junil Choi Receives Stephen O. Rice Prize
< Professor Junil Choi (second from the left) > Professor Junil Choi from the School of Electrical Engineering received the Stephen O. Rice Prize at the Global Communications Conference (GLOBECOM) hosted by the Institute of Electrical and Electronics Engineers (IEEE) in Hawaii on December 10, 2019. The Stephen O. Rice Prize is awarded to only one paper of exceptional merit every year. The IEEE Communications Society evaluates all papers published in the IEEE Transactions on Communications journal within the last three years, and marks each paper by aggregating its scores on originality, the number of citations, impact, and peer evaluation. Professor Choi won the prize for his research on one-bit analog-to-digital converters (ADCs) for multiuser massive multiple-input and multiple-output (MIMO) antenna systems published in 2016. In his paper, Professor Choi proposed a technology that can drastically reduce the power consumption of the multiuser massive MIMO antenna systems, which are the core technology for 5G and future wireless communication. Professor Choi’s paper has been cited more than 230 times in various academic journals and conference papers since its publication, and multiple follow-up studies are actively ongoing. In 2015, Professor Choi received the IEEE Signal Processing Society Best Paper Award, an award equals to the Stephen O. Rice Prize. He was also selected as the winner of the 15th Haedong Young Engineering Researcher Award presented by the Korean Institute of Communications and Information Sciences (KICS) on December 6, 2019 for his outstanding academic achievements, including 34 international journal publications and 26 US patent registrations. (END)
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging. As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE. He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA. Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
New Anisotropic Conductive Film for Ultra-Fine Pitch Assembly Applications
(Professor Paik(right) and PhD Candidate Yoon) Higher resolution display electronic devices increasingly needs ultra-fine pitch assemblies. On that account, display driver interconnection technology has become a major challenge for upscaling display electronics. Researchers have moved to one step closer to realizing ultra-fine resolution for displays with a novel thermoplastic anchoring polymer layer structure. This new structure can significantly improve the ultra-fine pitch interconnection by effectively suppressing the movement of conductive particles. This film is expected to be applied to various mobile devices, large-sized OLED panels, and VR, among others. A research team under Professor Kyung-Wook Paik in the Department of Materials developed an anchoring polymer layer structure that can effectively suppress the movement of conductive particles during the bonding process of the anisotropic conductive films (ACFs). The new structure will significantly improve the conductive particle capture rate, addressing electrical short problems in the ultra-fine pitch assembly process. During the ultra-fine pitch bonding process, the conductive particles of conventional ACFs agglomerate between bumps and cause electrical short circuits. To overcome the electrical shortage problem caused by the free movement of conductive particles, higher tensile strength anchoring polymer layers incorporated with conductive particles were introduced into the ACFs to effectively prevent conductive particle movement. The team used nylon to produce a single layer film with well-distributed and incorporated conductive particles. The higher tensile strength of nylon completely suppressed the movement of conductive particles, raising the capture rate of conductive particles from 33% of the conventional ACFs to 90%. The nylon films showed no short circuit problem during the Chip on Glass assembly. Even more, they obtained excellent electrical conductivity, high reliability, and low cost ACFs during the ultra-fine pitch applications. Professor Paik believes this new type of ACFs can further be applied not only to VR, 4K and 8K UHD display products, but also to large-size OLED panels and mobile devices. His team completed a prototype of the film supported by the ‘H&S High-Tech,’ a domestic company and the ‘Innopolis Foundation.’ The study, whose first author is PhD candidate Dal-Jin Yoon, is described in the October issue of IEEE TCPMT. Figure 1: Schematic process of APL structure fabrication. Figure 2: Proto-type production of APL ACFs.
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