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

NRI：协作研究：利用数据增强的接触力学加速机器人操作

• 批准号: 1637753
• 负责人: Alberto Rodriguez Garcia
• 金额: $ 43万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637753&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Accelerating; Robotic

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.

机器人的操作取决于机器人和物体之间的机械接触。 对机械接触的更好理解可以使更广泛的更灵活的操纵技术范围，从而使最大的社会利益（例如老年护理，灾难反应或手术）的应用。 该项目正在使用物理和数据融合对摩擦接触进行更广泛，更准确的了解。 该项目结合了对摩擦接触与新机器学习技术的基于物理学接触的最新进展，该技术应用于大量的实验数据。 一个引起人们关注的操作是操纵机器人抓手中的物体，即使抓手非常简单。 感兴趣的其他操作是处理混乱中的物体，并操纵柔性物体，例如服装。该项目正在攻击几个核心挑战：建模摩擦接触，建模变形，测量小动作和相互作用力，收集大量数据以及在封闭系统中进行学习的技术。 参数和半参数模型使项目能够应用工程模型，并通过观察数据增强了计划和控制。 新的机器学习技术，例如预测状态表示（PSRS），可以识别和建模以前隐藏的状态以及在闭环系统中的学习。 新的基础架构可以大规模收集相关，精确的数据。 该项目正在开发和采用机器人操纵领域，具有独特的操纵资源和仪器的组合，以提供大量高质量的实验数据。 主要结果是强大而实用的接触模型，因此机器人可以更灵活地和机会性地工作。