NRI: Collaborative Research: Accelerating Robotic Manipulation with Data-Enhanced Contact Mechanics

机器人的操作依赖于机器人与物体之间的机械接触。对机械接触的更好理解使更广泛、更灵活的操作技术成为可能，这反过来又使老年人护理、灾难应对或手术等应用获得最大的社会效益。该项目正在利用物理学和数据的融合，开发对摩擦接触的更广泛和更准确的理解。该项目将基于物理的摩擦接触理解方面的最新进展与应用于大量实验数据的新机器学习技术相结合。一个非常有趣的操作是操纵保持在机器人夹持器中的对象，即使夹持器非常简单。其他感兴趣的操作包括在杂乱中处理物体，以及操纵灵活的物体，如衣服。该项目正在解决几个核心挑战：摩擦接触建模、变形建模、测量微小运动和相互作用力、收集大量数据，以及开发在闭环系统中学习的技术。参数模型和半参数模型使项目能够应用通过观测数据增强的工程模型，以进行规划和控制。新的机器学习技术，如预测状态表示(PSR)，使得能够识别和建模先前隐藏的状态，以及在闭环系统中学习。新的基础设施能够大规模收集相关、准确的数据。该项目正在开发和使用机器人操纵竞技场，将操纵资源和仪器进行独特的组合，以提供大量高质量的实验数据。主要成果是稳健和实用的接触模型，使机器人能够更灵活和机会主义地工作。

Robotic manipulation depends upon mechanical contact between robot and object. A better understanding of mechanical contact enables a wider range of more flexible manipulation techniques, which in turn enables the applications of greatest societal benefit such as eldercare, disaster response, or surgery. This project is developing a broader and more accurate understanding of frictional contact, using a fusion of physics and data. The project combines recent advances in a physics-based understanding of frictional contact with new machine learning techniques applied to a large corpus of experimental data. One operation of great interest is manipulation of an object held in the robot gripper, even when the gripper is very simple. Other operations of interest are handling objects in clutter, and manipulation of flexible objects, such as clothing.The project is attacking several central challenges: modeling frictional contact, modeling deformation, measuring small motions and interaction forces, gathering large amounts of data, and developing techniques for learning in a closed-loop system. Parametric and semi-parametric models enable the project to apply engineering models enhanced with observation data, for both planning and control. New machine learning techniques such as predictive state representations (PSRs) enable identification and modeling of previously hidden state, as well as learning in closed-loop systems. New infrastructure enables gathering of relevant, precise data, on a large scale. The project is developing and employing a Robotic Manipulation Arena, with a unique combination of manipulation resources and instrumentation to provide high volumes of high quality experimental data. The primary outcomes are robust and practical contact models, so that robots can work more dexterously and opportunistically.