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

设计可以在人类周围工作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滋扰（从硬件故障到软件问题，错误校准以及不可预测的异常，如篡改和破坏）保持稳健。然而，观测和命令流具有相干性的事实表明，许多这些干扰可以被检测到，并通过一般方法自动减轻，这意味着非常低的设计努力。目前，机器人系统被发展为一组实现有向“计算图”的组件。本项目重点研究低级机器人感知运动信号故障/异常检测机制的理论方法、应用设计和参考实现。该系统在没有任何关于机器人配置的先验信息的情况下，通过被动观察计算图中暴露的信号来学习机器人和环境的模型，并基于该模型在增强计算图中实例化故障/异常检测组件。该项目邀请本科生和研究生参与先进机器人的设计和开发。预计该研究成果将对未来的机器人系统和机器学习产生重大影响。

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.