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

设计能够在人类周围操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具有健壮性，从硬件故障到软件问题，错误的校准，以及不可预测的异常，如篡改和破坏。然而，观察和命令流具有连贯性这一事实表明，这些干扰中的许多可以被检测到，并通过暗示非常低的设计努力的一般方法自动减轻。目前，机器人系统是作为一组组件来开发的，这些组件实现了有向的“计算图”。本项目的重点是针对低级机器人感应电机信号的故障/异常检测机制的理论方法、适用设计和参考实现。该系统在没有任何关于机器人配置的先验信息的情况下，应该通过对计算图中暴露的信号的被动观察来学习机器人和环境的模型，并基于该模型实例化扩展计算图中的故障/异常检测组件。该项目吸引本科生和研究生参与高级机器人设计和开发。预计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.