| Project/Area Number |
22K14221
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 20020:Robotics and intelligent system-related
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| Research Institution | Nagoya University |
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Principal Investigator |
Colan Jacinto 名古屋大学, 工学研究科, 研究員 (40936746)
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2023: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2022: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | compliant robotic hand / in-hand manipulation / reinforcement learning / sim-to-real / learning manipulation / adaptive compliance / robotic gripper / cable-driven mechanism / In-hand Manipulation / Interactive learning / Compliant gripper |
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| Outline of Research at the Start |
Human hands are capable of manipulating both rigid and deformable objects, independently of their shape or consistency. This project aims to provide robots with these human-like capabilities by developing an underactuated compliant gripper, an interactive learning framework and sim2real deployment.
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| Outline of Final Research Achievements |
Our project has focused on developing robotic grippers and hands for learning manipulation tasks. We proposed several innovative designs, including a cable-driven gripper with decoupled joints for effective grasping force estimation and a variation of anthropomorphic robotic fingers using linkage mechanisms to increase rigidity and pinch force. Additionally, we developed a three-finger underactuated robotic hand with compliant joints and cable-driven actuation, where each finger is actuated by a single motor managing both flexion and abduction movements. Significant progress has been made in enhancing the control mechanisms and interaction capabilities of this new hand gripper. We created a simulation environment to train in-hand manipulation of small objects, implementing and validating several reinforcement learning algorithms. Furthermore, we implemented Image-to-Image Contrastive Unpaired Translation (CUT) for realistic policy training inputs to facilitate sim-to-real transfer.
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| Academic Significance and Societal Importance of the Research Achievements |
Our research achievements enable safe and precise manipulation with robotic grippers and hands, promoting human-robot collaboration in several fields that require delicate manipulation such as assembly operations, wearable and field robotics, assistive devices, and prosthesis.
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