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Autonomous Systems Engineering Lab
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A lab that uses deep learning to create life-like intelligence.
AboutAbout the lab
Understanding and creating life-like intelligence.
The Autonomous Systems Engineering Lab studies artificial life and artificial intelligence under the theme of "understanding and creating life-like intelligence."
Using neural networks such as deep learning as tools, the lab applies them in a wide range of areas.
These include AI programs for board games and curling, reproducing the process by which people experience optical illusions, AI that supports sign language learning, and research on monitoring the behavior of zoo animals.
Under the motto "give everything to both research and play," the lab values building an environment where members can commit themselves without fear of failure.
Key terms
- Artificial life
- A research field that creates life-like behavior on computers and robots in order to study the mechanisms of life
- Deep learning
- A machine learning method in which a neural network with many stacked layers is trained on a large number of examples so that it learns to capture features
- Semi-supervised learning
- A machine learning method that trains on a combination of a small amount of labeled data and a large amount of unlabeled data
NewsLab news
An undergraduate student received the Outstanding Presentation Award at the 23rd Micro Symposium on Complex Systems
A master's student received the Excellent Presentation Award at the SICE System Integration Division Annual Conference (SI2023)
ResearchResearch themes
The lab aims to understand and create life-like intelligence. Using neural networks such as deep learning as tools, it conducts research on a wide range of subjects, from athletic movements and tactics to lung sounds and animal monitoring.
*Illustrative imageAI Evaluation of Bouldering
This is research on pose estimation, which captures a climber's movements from video. The lab tackled the problem that the body is hidden behind holds and hard to estimate, using semi-supervised learning, which learns from a small amount of labeled data, together with a technique that uses hold position information.
*Illustrative imageAI for Curling Tactics
The lab pursues two lines of research: analyzing match data from international and domestic tournaments to help teams develop tactics, and building strong AI for Digital Curling, a computer game.
*Illustrative imageDetecting Abnormal Lung Sounds with AI
As part of a project led by Hokkaido University that uses AI to detect abnormalities from biometric data collected by wearable devices, the lab works on lung sounds. It is building a model that finds abnormal sounds using AI trained only on normal lung sounds.
*Illustrative imageDetecting Estrus in an Endangered Species
This is research that uses only zoo surveillance camera footage to identify, with AI, behavior characteristic of the estrus period of the Tsushima leopard cat. Because no sensors need to be attached to the animals, they can be monitored without stress.
MembersFaculty
The members of the Autonomous Systems Engineering Lab consist of faculty members and students in the doctoral, master's, and undergraduate programs.
- ProfessorMasahito Yamamoto
- Associate ProfessorYasumasa Tamura
VoicesStudent voices
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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: “Kilobot robot swarm” asuscreative / CC BY-SA 4.0 (cropped)
- AI Evaluation of Bouldering: “Indoor Climbing Holds” Clay Junell / CC BY-SA 2.0 (cropped)
- AI for Curling Tactics: “Curling stones on rink with visible pebble” Felix from Canada / CC BY-SA 2.0 (cropped)
- Detecting Abnormal Lung Sounds with AI: “2023 Stetoskop” Jacek Halicki / CC BY-SA 4.0 (cropped)
- Detecting Estrus in an Endangered Species: “Tsushima Cat 001” Pontafon / CC BY-SA 3.0 (cropped)
- Related lab (調和系
工学研究室): “Snow removal in Sapporo ( )” MIKI Yoshihito / CC BY 2.0 (cropped) - Related lab (知能情報学研究室): “Quiet Electric Engine V1 (QUEEN V1) Experiment (GRC-2024-C-06530)” NASA Glenn Research Center / NASA/GRC/Sara Lowthian-Hanna / Public domain (cropped)
- Related lab (メディアダイナミクス研究室): “Aperture grille closeup” Selçuk Oral (drumex) / CC BY-SA 3.0 (cropped)
- Related lab (知能ロボットシステム研究室): “TOCABI black 3200px” Cadop / CC BY-SA 4.0 (cropped)
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