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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

基本信息

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

项目摘要

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.

该项目旨在设计和开发欠驱动机器人抓取器，该抓取器可以通过人类演示和交互式反馈来学习自适应柔性操作可变形物体。在第一年，我们完成了第一个目标，即开发一种紧凑的多自由度机器人夹持器，该夹持器具有解耦的手腕，可以实现每个关节的独立位置和力控制。我们使用开发的夹持器进行了微创手术应用的实验验证。采集不同刚度材料抓取时的致动器信号，训练高斯过程回归模型进行无传感器抓取力预测。设计了一个多目标逆运动学控制框架，可以同时处理多个约束条件下的关节位置控制。下一个目标是使用强化学习、模拟到真实技术和交互式反馈从人类演示中学习自适应遵从性。我们计划开发一个模拟环境，可以捕捉人类演示，并使用最先进的深度强化学习算法训练无模型策略。