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Manipulation Systems Lab
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The lab aims to give robots human-like dexterity in handling objects.
AboutAbout the lab
Robots that handle objects as dexterously as humans
The Manipulation Systems Lab conducts research aimed at making robots manipulate objects as dexterously as humans.
When robots and humans perform the same task, robots often cannot match human dexterity. The lab therefore aims to develop robots capable of dexterous object manipulation by combining technologies such as sensors and AI.
The lab also works on modeling the objects being manipulated, and on applying the results to industrial robots and manufacturing sites through joint research and other collaborations.
Key terms
- Manipulation
- The field concerned with handling objects, such as carrying or assembling them, using robot arms and hands.
- Roller hand mechanism
- A mechanism for a robot hand equipped with rollers. In this lab, it is used in research on separating stacked cloth one sheet at a time.
- Mechanical model and geometric model
- Mathematical or similar representations of the relationships among forces acting on an object, and of the object's shape and position.
NewsLab news
An undergraduate student received the Excellence Award at the 2024 Joint Workshop on Advanced Robotics
An undergraduate student received the Outstanding Student Award from the Robotics Society of Japan
An undergraduate student received the Excellence Award at the 2023 Joint Workshop on Advanced Robotics
ResearchResearch themes
When robots and humans perform the same task, there are many situations in which robots cannot match human dexterity. To address this, the lab aims to develop robots that can handle objects as dexterously as humans by combining technologies such as sensors and AI, and also works on object modeling and applications to industrial robots.
*Illustrative imageHuman-like dexterous object manipulation
The lab combines technologies such as sensors and AI to create robots that can handle objects as dexterously as humans. Examples include manipulation of Jenga blocks based on vision and force sensing, separating a single sheet from stacked cloth using a roller hand mechanism, and manipulation of flexible objects (wires and sheets).
*Illustrative imageModeling of object manipulation
To support dexterous object manipulation by robots, the lab models the objects to be manipulated using mechanical models and geometric models.
*Illustrative imageApplications to industrial robots
Through joint research and other collaborations, the lab works on applying its results to industrial robots and manufacturing sites. Examples include developing a teleoperation and teaching system using VR, mechanical analysis of failures during part assembly, and optimal sequence and path planning for tasks at multiple locations.
MembersFaculty
The members of the Manipulation Systems Lab are faculty, doctoral students, master's students, and undergraduate students.As of 2024
- ProfessorYasumichi Aiyama
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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: “Robotic ROV Manipulator Arm” BPLINFO / CC BY-SA 4.0 (cropped)
- Human-like dexterous object manipulation: “PR2 robot with advanced grasping hands” Oleg Alexandrov / CC BY-SA 3.0 (cropped)
- Modeling of object manipulation: “Dobot Magician in Robotics Laboratory 02” Giacomo Alessandroni / CC BY-SA 4.0 (cropped)
- Applications to industrial robots: “KUKA Industrial Robots IR” Mixabest / CC BY-SA 3.0 (cropped)
- Related lab (神経情報科学研究室): “Young blonde white girl sleeping on the side” Alexandre Bernier Marceau / CC BY-SA 4.0 (cropped)
- Related lab (三目研究室): “Bubble-rising” Atif Masood (FetchCFD) / CC BY 4.0 (cropped)
- Related lab (熱流体
制御・計測研究室): “COLOR IMAGE OF THE FLOW VISUALIZATION RIG - BLACK & WHITE IMAGE OF DIFFUSER EXPERIMENT RIG - NARA - 17448953” 作者不詳 / Public domain (cropped) - Related lab (生体情報処理研究室): “Neural network explain” TseKiChun / CC BY-SA 4.0 (cropped)
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