Learning in-hand manipulation for a compliant underactuated gripper with interactive human supervision
| 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 |
Granted (Fiscal Year 2022)
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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 | in-hand 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 Annual Research Achievements |
This project aims to design and develop underactuated robotic grippers that can learn adaptive compliance for soft manipulation of deformable objects through human demonstrations and interactive feedback.
In the first year, we accomplished the first objective of developing a compact multi-dof robotic gripper with a decoupled wrist that enables independent position and force control of each joint. We carried out experimental validation for minimally invasive surgical applications using the developed gripper. We collected actuator signals when grasping materials with different stiffness and trained a Gaussian process regression model for sensorless grip force prediction. We also devised a multi-objective inverse kinematics control framework that can handle joint position control under multiple constraints simultaneously.
The next objective is to learn adaptive compliance from human demonstrations using reinforcement learning, sim-to-real techniques and interactive feedback. We plan to develop a simulation environment that can capture human demonstrations and train a model-free policy using state-of-the-art deep reinforcement learning algorithms.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project is advancing well as the first objective was completed during the first year, with the design, prototyping, control and evaluation of a cable-driven gripper for compliant grip force. Progress on the next objectives has been started with a supervised regression model trained for sensorless grip force prediction.
The proposed design, control and evaluation of the developed gripper have been published in two journal papers and two international conferences.
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| Strategy for Future Research Activity |
The project is expected to proceed according to the initially approved plan without any changes. In the next project year, we plan to develop a simulation environment that can capture human demonstrations for manipulation of soft objects. A model-free policy will then be trained using state-of-the-art deep reinforcement learning algorithms. Sim-to-real strategies will be applied for transferring the learned manipulation strategy to the real robotic gripper, and interactive human feedback will be used for adapting the learned policy and deploying it again in the physical system.
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Report
(1 results)
Research Products
(4 results)
