| 研究課題/領域番号 |
22K14221
|
|---|
| 研究機関 | 名古屋大学 |
|---|
研究代表者 |
Colan Jacinto 名古屋大学, 工学研究科, 研究員 (40936746)
|
|---|
| 研究期間 (年度) |
2022-04-01 – 2024-03-31
|
| キーワード | in-hand manipulation / adaptive compliance / robotic gripper / cable-driven mechanism |
|---|
| 研究実績の概要 |
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.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
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.
|
| 今後の研究の推進方策 |
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.
|
| 次年度使用額が生じた理由 |
For prototyping the proposed gripper, we used existing actuators and control circuits. Next steps involve the extension to multiple cable-driven fingers of larger size, so we postponed the scheduled purchase of motors, mechanical parts and electrical parts for the next year. Moreover, since the first year focused on the mechanical design of the gripper, we will also purchase the deep learning workstation for the manipulation learning aspects during the second year.
|
