In our laboratory, we are conducting research with the aim of engineering realization of higher-order functions such as computational elucidation and reasoning of information processing in the brain. Another theme is to show the usefulness of brain-type information processing systems by applying brain-type information processing to real-world issues, breaking the limits of the information processing capabilities of classical artificial intelligence. is. In particular, we are developing human assistance technology.
Each member of this laboratory is researching with themes in fields such as neural networks, robots and artificial intelligence. Through graduation research, 4th year undergraduate students will learn how to proceed with research, write a dissertation, and present a presentation. Graduate School further deepen their themes, interact with other researchers, and disseminate the results in papers and conference presentations. We are also developing an outdoor autonomous mobile robot in cooperation with the first to third year undergraduate students of the research internship.

Brain type inference using recurrent neural network
Classic artificial intelligence (classical AI) internally expresses external world information as a symbol and realizes information processing such as inference by manipulating the symbol. However, there are two unsolved problems (symbol grounding problem and frame problem) in classical AI. Therefore, its information processing capacity is limited.
On the other hand, we are focusing on information processing in the brain. In the brain, information is expressed in a distributed manner as activity patterns of many nerve cells (neurons), and the patterns are dynamically converted according to the autonomous dynamics created by the network of neurons. Using these as hints, I am studying brain type inference methods using recurrent neural networks.

Hand / arm motion estimation from surface myoelectric potential signals using brain-type information processing system
We are aiming to develop human support technology that supports human cognitive and motor functions. For example, consider replacing the functions of the hands and arms with a robot arm. To do so, it is necessary to quickly estimate a person's movement intentions and plans. However, meaningful "movements" and "behaviors" that consist of a combination of several movements are very difficult to handle because they change both spatially and temporally.
In this regard, we propose a system that estimates the behavior of the hand and arm from the myoelectric potential signal that can be obtained from the skin surface of the arm, using the orbital attractor model, which is a type of recurrent neural net with continuous time dynamics. I am. Since this system can flexibly estimate complex behavior, it has great potential for application.

Development of outdoor autonomous mobile robot Progress-i
We are developing a robot that can safely and reliably judge and travel to the destination in an outdoor environment prepared for humans. Since it is outdoors, the range of activity is wide, and unexpected things such as pedestrians popping out, tires slipping, and sensors not functioning well due to the weather often occur. It is required to accurately estimate the position of the robot itself and move wisely while recognizing the environment flexibly.
This robot is planned to be used as part of a system that automatically inspects the equipment of a large-scale solar power generation facility (mega solar) in the future. A system that patrols tens of thousands of solar panels one by one, takes pictures from the back side of the panel with a thermal image camera, sends the data to the analysis server, and discovers places (hot spots) that have local heat. is.

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Introduction of Ken Yamane