NRI-Small: Improved safety and reliability of robotic systems by faults/anomalies detection from uninterpreted signals of computation graphs

NRI-Small：通过从计算图的未解释信号中检测故障/异常，提高机器人系统的安全性和可靠性

• 批准号: 1405259
• 负责人: Andrea Censi
• 金额: $ 67.26万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405259&HistoricalAwards=false
• 关键词: NRI; Small; Improved; safety; reliability

One of the main challenges to designing robots that can operate around humans is to create systems that can guarantee safety and effectiveness, while being robust to the nuisances of unstructured environments, from hardware faults to software issues, erroneous calibration, and less predictable anomalies, such as tampering and sabotage. However, the fact that the streams of observations and commands possess coherence properties suggests that many of these disturbances could be detected and automatically mitigated with general methods that imply very low design efforts. Currently, robotic systems are developed as a set of components realizing a directed "computation graph". This project focuses on theoretical methods, applicable designs, and reference implementation of a faults/anomalies detection mechanism for low-level robotic sensorimotor signals. The system, without any prior information about the robot configuration, should learn a model of the robot and the environment by passive observations of the signals exposed in the computation graph, and, based on this model, instantiate faults/anomalies detection components in an augmented computation graph.The project engages undergraduate and graduate students in advanced robotics design and development. It is expected the research results will have a significant impact on future robotic systems and machine learning.

设计可以在人类周围运行的机器人的主要挑战之一是创建能够保证安全性和有效性的系统，同时对非结构化环境的干扰具有鲁棒性，从硬件故障到软件问题，错误校准以及不可预测的异常，例如篡改和破坏。然而，事实上，观察和命令流具有一致性的属性表明，许多这些干扰可以检测到，并自动减轻与一般的方法，这意味着非常低的设计工作。 目前，机器人系统被开发为实现有向“计算图”的一组组件。该项目的重点是理论方法，适用的设计和参考实现的故障/异常检测机制，为低级别的机器人sensorimotor信号。该系统，没有任何关于机器人配置的先验信息，应该通过被动观察暴露在计算图中的信号来学习机器人和环境的模型，并且基于该模型，在增强计算图中实例化故障/异常检测组件。该项目吸引了高级机器人设计和开发的本科生和研究生。 预计研究结果将对未来的机器人系统和机器学习产生重大影响。