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Yairi-Takeishi Lab
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A lab working on artificial intelligence, from its fundamentals to applications in aerospace.
AboutAbout the lab
From "What is intelligence?" to how to put it to use.
The Yairi-Takeishi Lab (Intelligent Systems Engineering) conducts fundamental research on artificial intelligence in the broad sense, as well as research on its applications in fields such as aerospace.
The lab pursues both research that explores "what intelligence is" and research that considers "how artificial intelligence can be put to use."
Its wide range of machine learning and artificial intelligence topics includes research on detecting anomalies in data from satellites and other systems, and research on robot control.
In addition to the topics introduced here, the lab welcomes research proposals based on free-thinking ideas.
Key terms
- Machine learning
- A technology in which a computer learns rules and features from large amounts of data and uses them for prediction and decision-making
- Remaining useful life prediction
- Estimating from data how much longer a machine or device can continue to be used properly
- Reinforcement learning
- A method of teaching good ways of acting by using "rewards" based on the results of trial and error as a guide
NewsLab news
Paper: A doctoral student's paper on a control method for lunar landing was published in IEEE Access
Paper: A doctoral student's paper on control of soft robots was accepted at CDC 2026
Paper: A doctoral student's paper on remaining useful life prediction was published in IJPHM
Paper: A doctoral student's paper on learning soft robots from video was accepted at RA-L
Paper: A doctoral student's paper was accepted at the IFAC World Congress
ResearchResearch themes
The lab conducts fundamental research on artificial intelligence in the broad sense, as well as research on its applications in fields such as aerospace. It covers a wide range of topics, including anomaly detection and remaining useful life prediction from data, probabilistic inference, robot control, and the integration of machine learning with scientific models.
*Illustrative imageData-Driven Health Monitoring
The lab studies methods for detecting anomalies and predicting remaining useful life from data, with applications in mind for engineered systems such as satellites, industrial plants, and automobiles.
*Illustrative imageProbabilistic Inference and Its Applications
The lab studies methods for inferring quantities such as model parameters from data while taking uncertainty into account, along with their applications in fields including aerospace.
*Illustrative imagePath Planning and Control of Mobile Robots
The lab studies methods for computing the paths that robots moving on the ground or in the air should follow, and for controlling them along those paths. It also works on using reinforcement learning to make robots behave as intended.
*Illustrative imageIntegrating Machine Learning and Scientific Models
The lab studies methods for obtaining models by combining machine learning, which learns from data, with mathematical models based on theory such as equations of motion.
MembersFaculty
The members of the Yairi-Takeishi Lab include faculty, project researchers, administrative assistants, doctoral, master's, and undergraduate students, and visiting researchers.As of 2026
- ProfessorTakehisa YairiPublications ↗
- LecturerNaoya TakeishiPublications ↗
VoicesStudent voices
Compiled from answers by students who were in the lab. Ratings and reviews reflect individual students' experiences, not the lab's official views.
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “View of Hubble after Being Released from the Shuttle Atlantis ( )” NASA Hubble Space Telescope / Public domain (cropped)
- Data-Driven Health Monitoring: “Chemical plant at Kawasaki City” elminium / CC BY 2.0 (cropped)
- Probabilistic Inference and Its Applications: “Gaussian Process Regression” Cdipaolo96 / CC BY-SA 4.0 (cropped)
- Path Planning and Control of Mobile Robots: “Quadcopter Drone in flight” Project Kei / CC BY-SA 4.0 (cropped)
- Integrating Machine Learning and Scientific Models: “Chaos Theory & Double Pendulum - 1” Cristian V. / CC BY-SA 4.0 (cropped)
- Related lab (津江・中谷研究室): “Combustion research ( )” Oak Ridge National Laboratory / CC BY 2.0 (cropped)
- Related lab (中須賀・船瀬・五十里研究室): “FORESAIL-1 Flight Model” Petri Niemelä / CC BY-SA 4.0 (cropped)
- Related lab (小川・
小田切研究室): “BepiColombo spacecraft model” National Aeronautics and Space Administration (NASA) / Public domain (cropped) - Related lab (小野田・峯杉研究室): “First NASA ISINGLASS rocket launch” NASA / Public domain (cropped)
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