RI: Small: Dexterous Manipulation Using Predictive Thin-Shell Modeling

可变形物体的抓取和操纵提出了许多新的研究挑战，这些挑战比刚性物体的要求高得多。一个特别的挑战是要充分理解变形的物理学和建模的方式，可以使用的真实的机器人系统中存在的噪声和不确定性，并与实时约束的可变形对象。该项目将使用离线模拟来预测可变形物体的状态，这些物体被建模为薄壳（即布，织物，衣服），然后可以被机器人视觉/抓取系统识别，以拾取和操纵这些物体。智力优点包括在综合最先进的塑性弹性数值计算与数据库驱动的操纵方法，使机器人操纵可变形物体的一个重要步骤。 这包括织物模拟技术，该技术提供了必要的精度水平，其速度适合于与机器人抓取任务并行的在线计算。 这项研究的更广泛的影响包括：1）创建一个开源的，可扩展的，可变形对象的3-D数据库，用于向机器人和图形社区传播，2）扩展个人机器人助理新兴领域的工作环境范围，以及3）开发和教育一批新的科学家，他们将计算机图形学，计算力学和机器人技术领域联系起来。

Grasping and manipulation of deformable objects presents a host of new research challenges that are much more demanding than for rigid objects. A particular challenge is to fully understand the physics of deformation and to model deformable objects in a way that can be used by real robotic systems in the presence of noise and uncertainty and with real-time constraints. This project will use offline simulation to predict states of deformable objects modeled as thin-shells (i.e. cloth, fabric, clothing) that can then be recognized by a robotic vision/grasping system to pick up and manipulate these objects. The Intellectual Merit includes a significant step forward in the synthesis of state-of-the-art numerical computation of plasto-elastica with a database-driven manipulation approach to allow robots to manipulate deformable objects. This includes fabric simulation technology that provides the requisite level of accuracy at speeds amenable to online computation parallel to the robotic grasping task. The Broader Impacts of this research include 1) creation of an open source, extensible, 3-D database of deformable objects for dissemination to the robotics and graphics communities, 2) extending the range of working environments for the emerging field of personal robotic assistants, and 3) developing and educating a new class of scientists who bridge the fields of computer graphics, computational mechanics, and robotics.