NRI: FND: Physics-based training of robots for manipulation of ropes and clothes

NRI：FND：基于物理的机器人操纵绳索和衣服的训练

• 批准号: 1925360
• 负责人: Mohammad Khalid Jawed
• 金额: $ 75万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925360&HistoricalAwards=false
• 关键词: NRI; FND; Physics; based; training

This project enables collaborative robots to manipulate ropes and clothes in the real world using computer models without constant human monitoring. Seamless integration of robots, as aids to humans, into our daily life and manufacturing environments requires autonomous robotic manipulation of everyday objects. A broad class of these objects have one-dimensional (e.g. ropes) or two-dimensional (e.g. towels) geometry and are highly flexible. This flexibility and deformation can be seen in action everyday while tying shoelaces or folding clothes. Robots must be able to predict this deformation and act accordingly for successful manipulation of such objects. Typically robots are trained to perform these manipulation tasks through repetitive human demonstrations; the robots simply replicate the steps undertaken by the trainer. This project, in contrast, replaces training by demonstration with modeling in computer. The robots will employ numerical simulations to figure out the best policies for manipulation that are robust against uncertainties of the real world, e.g. friction and material defects. As such, a collaborative robot will be able to perform manipulation tasks right out of the box. Areas of application for this framework include typing knots in ropes, securing rigid objects using knots, and folding of clothes.The research objective of this project is to fundamentally understand robotic manipulation of flexible objects (ropes & clothes) using model-based training. The team of researchers will develop physics-based simulation tools for the mechanics of deformable structures and demonstrate the application of fast and efficient simulations to train robots. To overcome the barriers associated with translating models to the real world, the researchers will use simulations, in conjunction with optimization, to formulate policies that are robust against uncertainties, e.g. friction and material defects. The goal is simulation-based training of cobots that is ready for application in the real world; this will largely remove the painstaking training process by physical demonstration required for collaborative robots. An open-source software repository, similar to App Stores for smart-phones, is envisioned that will host training programs for a variety of applications. The strength of this approach will be demonstrated through autonomous folding of towels and tying of knots to secure objects.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.

该项目使协作机器人能够使用计算机模型在现实世界中操纵绳索和衣服，而无需不断监控。机器人无缝整合作为对人类的援助，进入我们的日常生活和制造环境需要对日常物体的自动机器人操纵。这些物体的一定类类具有一维（例如绳索）或二维（例如毛巾）几何形状，并且具有高度灵活性。每天都可以在绑鞋带或折叠衣服的同时，可以在行动中看到这种灵活性和变形。机器人必须能够预测这种变形并采取相应的行动，以成功地操纵此类对象。通常，训练机器人通过重复的人类示范来执行这些操纵任务。机器人简单地复制了培训师的步骤。相反，该项目用计算机中的建模代替了培训。这些机器人将采用数值模拟来找出对操纵的最佳政策，这些政策可与现实世界的不确定性，例如摩擦和物质缺陷。因此，协作机器人将能够在开箱即用执行操作任务。该框架的应用领域包括在绳索上打结，使用结固定刚性对象以及折叠衣服。该项目的研究目标是从根本上了解使用基于模型的培训的灵活物体（Ropes＆Clote）的机器人操纵。研究人员团队将开发基于物理的仿真工具，用于可变形结构的机制，并证明快速有效的仿真在训练机器人中的应用。为了克服与将模型转换为现实世界相关的障碍，研究人员将使用模拟并结合优化，以制定强大的不确定性政策，例如摩擦和物质缺陷。目的是基于模拟的柯比特培训，该培训已准备好在现实世界中应用；这将在很大程度上通过协作机器人所需的身体演示来消除艰苦的训练过程。设想了一个开源软件存储库，类似于智能手机的应用商店，它将托管各种应用程序的培训计划。这种方法的优势将通过自主折叠毛巾和结结而固定以确保对象的能力来证明。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响评估标准通过评估来支持的。

项目成果

Automated Stability Testing of Elastic Rods With Helical Centerlines Using a Robotic System

使用机器人系统对具有螺旋中心线的弹性杆进行自动稳定性测试

• DOI: 10.1109/lra.2021.3138532
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Tong, Dezhong;Borum, Andy;Jawed, Mohammad Khalid
• 通讯作者: Jawed, Mohammad Khalid

A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing

• DOI: 10.1109/iros51168.2021.9636267
• 发表时间: 2021-09
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Yayun Du;Bhrugu Mallajosyula;Deming Sun;Jingyi Chen;Zihang Zhao;Mukhlesur Rahman;M. Quadir;M. Jawed

Rapidly encoding generalizable dynamics in a Euclidean symmetric neural network

• DOI: 10.1016/j.eml.2022.101925
• 发表时间: 2022-03
• 期刊: Extreme Mechanics Letters
• 影响因子: 4.7
• 作者: Qiaofeng Li;Tianyi Wang;V. Roychowdhury;M. Jawed

Preemptive Motion Planning for Human-to-Robot Indirect Placement Handovers

人机间接放置切换的先发性运动规划

• DOI: 10.1109/icra46639.2022.9811558
• 发表时间: 2022
• 期刊: 2022 International Conference on Robotics and Automation (ICRA
• 作者: Choi, Andrew;Jawed, Mohammad Khalid;Joo, Jungseock
• 通讯作者: Joo, Jungseock

Supracellular Measurement of Spatially Varying Mechanical Heterogeneities in Live Monolayers.

• DOI: 10.1016/j.bpj.2022.08.024
• 发表时间: 2022-08
• 期刊: Biophysical journal
• 影响因子: 3.4
• 作者: Alexandra Bermudez;Zachary Gonzalez;Bao Zhao;Ethan Salter;Xuanqing Liu;Leixin Ma;M. Jawed;Cho-Jui Hsieh;Neil Y. C. Lin