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手，它可以通过人体演示和交互式反馈来学习自适应柔顺性，以软操纵可变形物体。第一年，我们实现了开发具有解耦功能的紧凑型多自由度机器人抓手的第一个目标手腕，可以独立控制每个关节的位置和力量。我们使用开发的夹具对微创手术应用进行了实验验证。我们在抓取具有不同刚度的材料时收集了执行器信号，并训练了用于无传感器抓握力预测的高斯过程回归模型。我们还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下的关节位置控制。下一个目标是使用强化学习、模拟真实技术和交互式反馈从人体演示中学习自适应顺应性。我们计划开发一个模拟环境，可以捕获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。