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Intelligent Robotics and Perceptual Information Processing Lab
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The lab studies intelligent robotics, using deep learning to process robot sensor data.
AboutAbout the lab
Interpreting sensor information with deep learning
The Human-Centered Robotics Lab uses robotics technology grounded in mechanical engineering to study the mathematical and mechanical understanding of the advanced motor intelligence of the human body, and its engineering applications.
Associate Professor Kazuto Nakashima specializes in intelligent robotics and perceptual information processing in this lab, and lists deep learning, soft robots, and 3D LiDAR as his research keywords.
The lab works with data from 3D range sensors (3D LiDAR) and with state estimation for robot hands that have soft fingers.
Associate Professor Nakashima belongs to the Faculty of Information Science and Electrical Engineering, and joins this lab through the Department of Mechanical Engineering in the Faculty of Engineering, Graduate School of Engineering.
Key terms
- 3D LiDAR
- A sensor that measures the distance to surrounding objects and captures their three-dimensional shape. Also called a 3D range sensor
- Proximity sensor
- A sensor that measures the distance to a nearby object before touching it. Here it is mounted on a robot finger
- Constant curvature model
- An approach that treats a bending finger as a curve with constant curvature in order to represent its shape. Used for state estimation of soft fingers
NewsLab news
A paper first-authored by a master's student was published in the Journal of Robotics and Mechatronics
A paper first-authored by a doctoral student was published in the Journal of Robotics and Mechatronics
ResearchResearch themes
The lab studies how to understand the advanced motor intelligence of the human body from mathematical and mechanical perspectives and apply it in engineering. Associate Professor Nakashima's research themes are state estimation for soft robots and missing-data restoration and domain adaptation for 3D LiDAR data, and Kyushu University's researcher information lists deep learning as a keyword.
*Illustrative imageState estimation for soft fingers
Soft fingers have no encoders to measure joint angles, which makes it difficult to determine the fingertip position during contact. The lab studies a method that estimates the state of the finger by combining proximity sensors mounted on the finger with a constant curvature model.
*Illustrative imageMissing-data restoration and domain adaptation for 3D LiDAR
Using deep learning, the lab studies missing-data restoration, which fills in gaps in data obtained with 3D range sensors (3D LiDAR), as well as domain adaptation.
MembersFaculty
The lab's members consist of faculty, secretaries, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorKenji Tahara
- Associate ProfessorHikaru Arita
- Associate ProfessorKazuto Nakashima
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: “Lidar P1270901” David Monniaux / CC BY-SA 3.0 (cropped)
- State estimation for soft fingers: “Soft Robotics” Oregon State University / CC BY-SA 2.0 (cropped)
- Missing-data restoration and domain adaptation for 3D LiDAR: “Ouster OS1-64 lidar point cloud of intersection of Folsom and Dore St, San Francisco” Daniel L. Lu / CC BY 4.0 (cropped)
- Related lab (量子コンピューティングシステム研究センター): “IBM Quantum Computer Demo at ITUWTSA 2024, Delhi 2” Dev Jadiya / CC BY-SA 4.0 (cropped)
- Related lab (光・量子プロセス研究開発センター): “Excimer-Laser-MEL80” Philos2000 / CC BY-SA 3.0 (cropped)
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