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真实的世界中操纵绳子和衣服，而无需持续的人类监控。将机器人作为人类的辅助工具无缝集成到我们的日常生活和制造环境中，需要机器人对日常物体进行自主操作。这些物体中的一大类具有一维（例如绳子）或二维（例如毛巾）几何形状，并且具有高度柔性。这种灵活性和变形可以在每天系鞋带或折叠衣服时看到。机器人必须能够预测这种变形并采取相应的行动才能成功操纵此类物体。通常，机器人通过重复的人类演示来训练执行这些操作任务;机器人只是复制训练员所采取的步骤。相反，这个项目用计算机建模取代了演示培训。机器人将采用数值模拟来找出最佳的操作策略，这些策略对真实的世界的不确定性（例如摩擦和材料缺陷）具有鲁棒性。因此，协作机器人将能够执行开箱即用的操作任务。该框架的应用领域包括在绳子上打绳结、使用绳结固定刚性物体和折叠衣服。本项目的研究目标是使用基于模型的训练从根本上理解机器人对柔性物体（绳子和衣服）的操作。研究团队将为可变形结构的力学开发基于物理的仿真工具，并展示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到真实的世界相关的障碍，研究人员将使用模拟与优化相结合，以制定对不确定性（例如摩擦和材料缺陷）具有鲁棒性的政策。目标是基于模拟的协作机器人训练，准备在真实的世界中应用;这将在很大程度上消除协作机器人所需的物理演示的艰苦训练过程。设想建立一个类似于智能手机应用程序商店的开放源码软件库，为各种应用程序举办培训课程。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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