When we humans are placed in a new place or environment, we are able to understand the situation and respond to things to some extent based on our previous experiences. When recognizing a kitchen, even in a house that you are visiting for the first time, you can determine that "this is a kitchen" by looking at the objects there, such as the refrigerator, stove, frying pans, and so on. This kind of "common sense" use of knowledge is only possible because we are human, but it is a completely different story with robot learning.
Existing service robots that help with household chores can carry objects to a predefined position only after receiving a detailed description of “what is where”. Therefore, they must learn “what is where,” namely, the relationship between objects and places based on the definitions of what words—for example, “cup” or “plate”—refer to, using a large amount of data. This high learning cost is a big issue. A robot cannot understand a vague command like "bring me a cup” unless the location and other details are specified first; it must complete a series of tasks to be able to operate smoothly in a living space.
This led Hasegawa to focus on the concept of probabilistic logical inference, which is similar to human common sense. In his study, the results of a questionnaire regarding the location of objects were used to create knowledge similar to human common-sense knowledge. By combining this knowledge with the robot's own knowledge of visual and other information obtained in the experimental environment, Hasegawa created a system that could improve the efficiency of data learning and enable the prediction of the location of undefined objects.

Experiments conducted in a simulated environment based on a virtual living space showed that learning cost could be reduced by a factor of 1.6 or more compared to existing methods. These groundbreaking research results were duly recognized with the Best Paper Award and the SICE International Young Authors Award at the 2023 IEEE/SICE International Symposium on System Integrations (SII2023). Hasegawa has since presented the results of an experiment conducted in a real environment, and he is in the process of conducting research for the practical application of this technique.

While producing outstanding results, Hasegawa is constantly reflecting on the problems with his own research, and he is always on the lookout for new research approaches. The issue with using probabilistic logical inference was that the vocabulary that the robot could use was limited. To solve this problem, Hasegawa focused on large language models, which use large amounts of text data to perform language processing tasks such as sentence generation, machine translation, and answering questions.

The robot in this study, which employs a large language model, has performed well and received high praise, including a second place finish in the @Home Domestic Standard Platform League Open Challenge at the RoboCup Japan Open 2023, and this study is expected to lead to the creation of more versatile service robots.

Hasegawa already has a wealth of awards and research achievements at the doctoral stage. He pours himself into his research every day, but what led him to become fascinated with robots was a human-like receptionist android that he saw at the Aichi Expo when he was in elementary school.

With an overflowing desire to conduct research, Hasegawa entered the Department of Robotics in the College of Science and Engineering at Ritsumeikan University. While learning about controls and technology, he also gained experience designing robots, including cutting his own aluminum plates as a member of the Robotics Club. In order to get closer to realizing his dream, he entered the Graduate School of Information Science and Engineering where he could specialize in research on intelligent service robots.

Unlike their hectic undergraduate years, graduate students who set their own research topics have more time to carefully study prior research and related literature. Hasegawa says that because he is able to sit back, accumulate knowledge, and contemplate deeply, he is able to engage in intensive research. Another appealing feature of graduate school life is the opportunity to interact with and be stimulated by a wide variety of researchers because there are more opportunities to participate in academic conferences.

Hasegawa was also chosen for the Ritsumeikan University NEXT Student Fellowship Program. This is a program that helps Fellows deepen their expertise and acquire a broad range of research perspectives while working with talented researchers who are conducting cutting-edge research in a diverse array of fields. According to Hasegawa, the program also offers many opportunities to interact with other Fellows in both the humanities and the sciences.

Hasegawa seems to be leading a rewarding graduate school life, where he spends his time in friendly competition with researchers who are active at the vanguard of their respective fields.

In order to realize a dream he has had since elementary school, Hasegawa says he would like to pursue a career as a researcher.

Although he is busy writing his dissertation, Hasegawa says with a smile that it is not unpleasant. Expectations are high for the yet-to-be-seen future he will create. Who knows? It may not be long before we see cat-shaped robots around the world.