| Project/Area Number |
23K26071
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| Project/Area Number (Other) |
23H01376 (2023)
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Multi-year Fund (2024)
Single-year Grants (2023) |
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| Section | 一般 |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
タ デゥックトゥン 東京大学, 大学院情報理工学系研究科, 助教 (20869226)
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| Co-Investigator(Kenkyū-buntansha) |
千葉 滋 東京大学, 大学院情報理工学系研究科, 教授 (80282713)
川原 圭博 東京大学, 大学院工学系研究科(工学部), 教授 (80401248)
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| Project Period (FY) |
2024-04-01 – 2027-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥18,980,000 (Direct Cost: ¥14,600,000、Indirect Cost: ¥4,380,000)
Fiscal Year 2026: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2025: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2024: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2023: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
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| Keywords | Body Design / Modularization / Morphology / Soft Sensors / Soft Actuator / Soft Robots / Soft sensors / Soft robots |
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| Outline of Research at the Start |
- Vision: Robust and versatile printable soft-bodied robots for environment exploration, object manipulation, and human interaction.
- Challenges: We lack a systematic approach to body design, fabrication, and control of the continuum of soft-bodied robots.
- Proposal: We will develop hierarchical data-driven learning-based algorithms (reinforcement and curriculum learning) to guide modular soft-bodied robots design and controlling process.
- Impact: Accelerate the development and adoption of soft robotics by helping design versatile soft robots for multiple tasks.
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| Outline of Annual Research Achievements |
We developed a hybrid robotic gripper designed to grasp thin, large, and deformable objects such as a single sheet of paper. The gripper integrates a soft pneumatic ring surrounding a rigid revolute joint, mimicking the adaptive grasping behavior of human fingers. In our experiments, we evaluated the gripper’s performance on paper of varying thicknesses, focusing on grasp stability, adaptability, and efficiency. The results provide insights into how hybrid soft-rigid mechanisms can handle flexible materials more effectively than conventional grippers. This research has been accepted for presentation at ICRA 2025 and Robomech 2025. We are currently considering filing a patent based on this design.
In parallel, we are developing a learning-based reservoir computing framework, PhysRes, to model and predict the behavior of a soft actuator driven by a liquid-gas phase change. We employ a motion capture system to track key landmark movements on the actuator pouch, which serve as proxies for its volumetric profile. These movements reflect the actuator’s internal state under constant room temperature, atmospheric pressure, and electrical input. PhysRes learns the dynamics from prior motion sequences and achieves a normalized root mean squared error of 0.0041 in estimating control points, with a volumetric error of 0.0160. To demonstrate practical potential, we designed a dual-pouch actuator-based gripper capable of soft object manipulation. This work is currently under review for presentation at IEEE IROS 2025 in Hangzhou, China.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The project is being carried out smoothly.
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| Strategy for Future Research Activity |
In the next phase of the project, we will focus on developing a hybrid soft/rigid robotic hand that comprised from multiple soft/rigid beams put in a star topology. In a parallel configuration, the soft fingers will interact with the grasping object through contact points. The soft fingers also have mutual influence through the body of the grasping object. We will use evolution-based and learning-based algorithms to design the variable stiffness of each flexible beam, the frictional surfaces of the contact points, and the gripping poses to optimize for each grasping scenario.
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