Learning in-hand manipulation for a compliant underactuated gripper with interactive human supervision

通过交互式人类监督，学习顺应欠驱动夹具的手动操作

• 批准号: 22K14221
• 负责人: Colan Jacinto
• 金额: $ 2.91万
• 依托单位: Nagoya University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Early-Career Scientists
• 财政年份: 2022
• 资助国家: 日本
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K14221/
• 关键词: in-hand manipulation; adaptive compliance; robotic gripper; cable-driven mechanism; In-hand Manipulation; Interactive learning; Compliant gripper

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.

该项目旨在设计和开发不足的机器人抓手，可以通过人类的演示和交互式反馈来学习适应性合规性，以软操作可变形物体。第一年，我们完成了第一个目标，即使用脱钩的腕部，可以独立于每个关节的位置和力量控制。我们使用已发达的抓手对微创手术应用进行了实验验证。当抓住具有不同刚度的材料并训练了无传感器握力预测的高斯工艺回归模型时，我们收集了执行器信号。我们还设计了一个多目标逆运动控制框架，该框架可以同时处理多个约束下的关节位置控制。下一个目标是使用强化学习，SIM到现实的技术和交互式反馈从人类示范中学习适应性依从性。我们计划开发一个模拟环境，该环境可以使用最先进的深入强化学习算法来捕捉人类示威并训练无模型的政策。

项目成果

Sensorless grip force estimation of a cable-driven robotic surgical tool based on Gaussian Process Regression

基于高斯过程回归的电缆驱动机器人手术工具的无传感器握力估计

• 发表时间: 2022
• 作者: Jacinto Colan;Yasuhisa Hasegawa
• 通讯作者: Yasuhisa Hasegawa

Constrained motion planning for a robotic endoscope holder based on Hierarchical Quadratic Programming

基于分层二次规划的机器人内窥镜支架约束运动规划

• 发表时间: 2023
• 作者: Jacinto Colan;Ana Davila;Yasuhisa Hasegawa
• 通讯作者: Yasuhisa Hasegawa

OpenRST: An Open Platform for Customizable 3D Printed Cable-Driven Robotic Surgical Tools

• DOI: 10.1109/access.2023.3236821
• 发表时间: 2023-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Colan, Jacinto;Davila, Ana;Hasegawa, Yasuhisa
• 通讯作者: Hasegawa, Yasuhisa