Information Technology and Human Factors

From IT infrastructure to application technology: make, connect, use

The rapid growth of devices (smartphone, tablets, robots, wearables, etc.) and the Internet has increased the amount of information that is being produced and accessed by society. In order to better utilize the data produced from millions of devices and systems, we are conducting research and development in a wide range of fields at the interface between information technologies and human factors. Our mission is to engage and enrich the public through the research and development of intelligent systems combining computational and physical capabilities for human use. A key component of our mission is making new discoveries in the hardware and software that interacts with physical devices to sense and change the state of the real world. Our discoveries will lead to industry innovations and contribute to the advancement of society by facilitating the interaction of humans with cyber-physical systems.

Information Technology and Human Factors Pamphlet

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New Research Results

From “AI That Sees a Single Object” to “AI That Compares Multiple Objects”

Researchers at AIST have developed a point-cloud language model that can understand and explain geometric relationships among multiple objects.
Comparing multiple objects or parts to identify differences in their shapes and their spatial relationships is a critical process in manufacturing. Although AI technology has increasingly been adopted in manufacturing in recent years, conventional vision-language models have been limited to recognizing and describing single objects. This makes it difficult for them to understand the geometric relationships among multiple objects. To address this limitation, the researchers constructed “Multi-Object in 3D (MO3D),” a dataset comprising approximately 70,000 high-quality point-cloud samples, each paired with question-and-answer annotations. MO3D serves as a new training and evaluation framework for comparing multiple objects and understanding their geometric relationships. Using this dataset, the researchers developed the “Multi 3D Large Language Model (Multi-3DLLM),” a point-cloud language model that can compare multiple objects at the part level and explain the details in natural language such as “which components are joined together” and “where their shapes differ.” In evaluation experiments, our proposed model surpassed the performance of existing vision-language models. For all description tasks (object comparison, object joining, and object changes) using MO3D, Multi-3DLLM demonstrated improved performance, with its question accuracy rate increasing by about 1.8 times compared to conventional methods. This technology realizes an AI model that can understand geometric relationships among multiple objects and explain them in words. As a result, it is expected to contribute to improved work efficiency in various fields (ex, design and manufacturing) including robot-assisted part sorting, assembly support, shape comparison, and editing support in 3D design software. Furthermore, the MO3D developed in this research is expected to advance physical AI research on understanding the geometric relationships between multiple objects.
The research results were presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (June 3-7, 2026, Denver, USA). In addition, the point cloud language model “Multi-3DLLM” and the “MO3D” dataset developed for this study are available on GitHub:
(https://github.com/KohsukeIde/BeyondSIngleObject).

Development of a point-cloud language model capable of understanding and explaining the geometric relationships between multiple objects

Streamlining Protein Function Prediction

Researchers at AIST have developed a machine learning approach that uses molecular simulations and protein language models to accurately predict protein functional values from a small amount of experimental data.
In recent years, researchers have increasingly used machine learning methods to predict protein functions for designing functional proteins. However, this requires significant time and material costs due to the need for large amounts of experimental observations as training data. As a result, methods that use computational values for pseudo-training data along with experimental data have gained attention. Although these methods were previously used to predict protein stability, expending their scope to include predictions of binding affinity and enzyme activity is necessary to design functional proteins tailored to specific purposes.
We have developed a novel method to predict protein functional values. This method takes advantage of functional values computed via molecular simulation and protein language models as pseudo-training data. It achieves high accuracy in predicting functional value even with limited experimental data. Furthermore, we have expanded its applicability beyond protein stability to include binding affinity, enzyme activity, cytotoxicity, and fluorescence intensity. This achievement enables more efficient development of functional proteins compared to existing methods.

Improving the accuracy of protein functional value prediction through data augmentation

Open Innovation Laboratory

Since FY 2016, as a part of the “Open Innovation Arena concept” promoted by the Ministry of Economy, Trade and Industry (METI), AIST has created the concept of “open innovation laboratories” (OILs), collaborative research bases located on university campuses, and has been engaged in their provision. We are planning to establish more than ten OILs by FY 2020.

AIST will merge the basic research carried out at universities, etc. with AISTʼs goal-oriented basic research and applied technology development, and will promote bridging research and evelopment and industry by the establishment of OILs.

Cooperative Research Laboratories

In order to conduct research and development more closely related to strategies of companies, we have established collaborative research laboratories, bearing partner company names.

Partner companies provide their researchers and funding, and AIST provides research resources, such as its researchers, research facilities, and intellectual property. The loaned researchers of companies and AIST researchers jointly conduct research and development.

By setting up cooperative research laboratories, we will accelerate the commercialization of our goal-oriented basic research and application research with partner companies.