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NRI: FND: Controllable Compliance: A New Robotic Arm for Contact-Rich Manipulation

NRI：FND：可控合规性：用于富接触操作的新型机械臂

基本信息

批准号：
1830425
负责人：
John Whitney
金额：
$ 75万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830425&HistoricalAwards=false
关键词：
NRI FND Controllable Compliance New

项目摘要

People with disabilities, the infirm, and the elderly would benefit from capable service robots in the home that could assist with daily living tasks. Robots with arms and hands that have human-like abilities could help retrieve dropped or distant objects, and help with cooking, cleaning, and grooming. This would increase people's quality of life and independence, and greatly reduce the burden on their caregivers. While robots are found in almost every factory today, they live behind safety barriers, out of reach of people. Traditional robots are good at controlling their motion, but bad at controlling the forces they apply when touching objects; essentially, they are precise but not gentle. This research introduces a new way to design robots that allows them to easily feel when they are in contact with people and objects. A new design allows all of the motors that move the arms to be located in the robot's base. A traditional robot has a motor mounted at each joint in the arm, making the robot very heavy. When robots are lighter and move more gently, it does not just make them safer, but it also makes it easier to program them to learn how to pick up and manipulate objects. Traditionally, robots depend only on cameras to detect objects and plan how to grab them, but in this research, robots will also adapt and learn to interact with objects through their sense of touch.Reliable and safe manipulation is a prerequisite for the vision of autonomous and semi-autonomous assistive robots, particularly towards the goal of increasing the length and quality of time that people can live independently. The key challenge is developing manipulator hardware that can move accurately but gently and supports safe contact-rich physical interaction with people and delicate objects. In this work a remote-direct-drive (RDD) configuration is used: all motors are mounted in the base, remotely connected to joints in the arm and hand via rolling-diaphragm sealed hydrostatic transmissions, which keeps the arm moving mass very small. Large radius direct-drive motors are used to achieve minimal joint static friction and allow joint compliance to be modulated over a wide range. Thus, the arm exhibits smooth second order joint dynamics, making it easier to plan contact-rich trajectories using optimization methods or learn contact-rich policies using off-line deep reinforcement learning. Moreover, the controllable compliance will enable our manipulation policies to use compliance when needed to accommodate environmental uncertainty but high stiffness when high precision is required. This type of manipulator has the potential to be lighter, lower friction, and more inherently backdrivable than traditional robotic arms, providing increased safety for human-robot interaction, and increasing the range of physical human assistance tasks that can be undertaken by autonomous systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

残疾人、体弱者和老年人将受益于家中有能力的服务机器人，这些机器人可以帮助完成日常生活任务。具有类似人类能力的手臂和手的机器人可以帮助找回掉落或远处的物体，并帮助烹饪，清洁和梳理。这将提高人们的生活质量和独立性，并大大减轻其照顾者的负担。虽然今天几乎每个工厂都有机器人，但它们生活在安全屏障后面，远离人类。传统的机器人擅长控制它们的运动，但不擅长控制它们在接触物体时施加的力;本质上，它们是精确的，但不是温柔的。这项研究介绍了一种设计机器人的新方法，使它们在与人和物体接触时能够轻松感受。一种新的设计允许所有移动手臂的电机位于机器人的底座上。传统的机器人在手臂的每个关节上都安装了一个电机，这使得机器人非常重。当机器人更轻，移动更轻柔时，它不仅使它们更安全，而且还使它们更容易编程来学习如何拾取和操纵物体。传统上，机器人仅依靠摄像头来探测物体并计划如何抓取物体，但在这项研究中，机器人还将通过触觉来适应和学习与物体互动。可靠和安全的操作是自主和半自主辅助机器人视觉的先决条件，特别是朝着增加人类独立生活时间的长度和质量的目标。关键的挑战是开发能够准确但温和地移动的机械手硬件，并支持与人和微妙物体的安全接触丰富的物理交互。在这项工作中，使用远程直接驱动（RDD）配置：所有电机都安装在底座上，通过滚动膜片密封的静压传动装置远程连接到手臂和手部的关节，这使手臂的移动质量非常小。大半径直接驱动电机用于实现最小的关节静摩擦，并允许在宽范围内调节关节顺应性。因此，手臂表现出平滑的二阶关节动力学，使得使用优化方法规划接触丰富的轨迹或使用离线深度强化学习学习接触丰富的策略变得更容易。此外，可控的顺应性将使我们的操纵策略能够在需要时使用顺应性以适应环境不确定性，但在需要高精度时使用高刚度。这种类型的机械手有可能比传统的机械臂更轻，摩擦力更低，并且更固有地可反向驱动，从而为人机交互提供更高的安全性，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

On-Robot Learning With Equivariant Models

DOI：
发表时间：
2022-03
期刊：
影响因子：
0
作者：
Dian Wang;Ming Jia;Xu Zhu;R. Walters;Robert W. Platt
通讯作者：
Dian Wang;Ming Jia;Xu Zhu;R. Walters;Robert W. Platt

Learning Bayes Filter Models for Tactile Localization

学习用于触觉定位的贝叶斯滤波器模型

DOI：
发表时间：
2020
期刊：
Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Kelestemur, Tarik;Keil, Colin;Whitney, John;Platt, Robert;Padir, Taskin
通讯作者：
Padir, Taskin

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection