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

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子和衣服，而无需持续的人类监控。机器人作为人类的助手，要无缝地融入我们的日常生活和制造环境，需要机器人自主地操纵日常物品。这些物体的很大一类具有一维(例如绳索)或二维(例如毛巾)的几何形状，并且具有高度的灵活性。这种弹性和变形在系鞋带或折叠衣服的日常活动中随处可见。机器人必须能够预测这种变形，并相应地采取行动，才能成功地操纵这些物体。通常，机器人是通过重复的人类演示来训练机器人执行这些操作任务的；机器人只是重复训练员采取的步骤。相比之下，这个项目用计算机建模取代了以演示代替培训。机器人将使用数值模拟来找出对现实世界的不确定因素(如摩擦和材料缺陷)具有健壮性的最佳操纵策略。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索上打结，使用绳结固定刚性物体，以及折叠衣服。本项目的研究目标是通过基于模型的培训，从根本上了解机器人对柔性物体(绳索和衣服)的操纵。研究小组将为可变形结构的力学开发基于物理的模拟工具，并演示快速有效的模拟在训练机器人方面的应用。为了克服与将模型转换到现实世界相关的障碍，研究人员将使用模拟和优化相结合的方式来制定对不确定因素(如摩擦和材料缺陷)具有健壮性的政策。其目标是对Cobots进行基于模拟的训练，以便在现实世界中应用；这将在很大程度上消除协作机器人所需的通过物理演示进行艰苦训练的过程。一个类似于智能手机应用商店的开源软件库被设想为将托管各种应用程序的培训计划。这种方法的力量将通过自动折叠毛巾和打结来保护物体来展示。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.