CPS: Medium: Learning to Sense Robustly and Act Effectively

信息物理系统的物理环境是无限复杂的，在时间和空间上不断变化。嵌入在物理世界中的具体化的信息物理系统将接收高带宽的感官信息流，并且可以具有多个具有连续控制信号的效应器。除了世界的动态变化，网络物理系统本身的属性？它的传感器和效应器随着时间的推移而改变。它如何科普这种复杂性？这一提议背后的假设是，一个成功的网络物理系统需要成为一个学习代理，从自己的经验中学习其传感器，效应器和环境的属性，并随着时间的推移而适应。受人类发展学习的启发，主张空间、物体、动作等基本概念，对于这样一个学习主体来说，抽象和控制其世界的复杂性是必不可少的。为了弥合与物理环境的连续交互与支持有效规划的离散符号描述之间的差距，代理将需要通过抽象关系链接的这些基础域的多个表示。 为了实现这一目标，该团队正在开发对象语义层次（奥什），它展示了学习代理如何为与之交互的对象创建表示层次。奥什说明如何？对象抽象？将传感器流中的不确定性分解为对象模型和对象轨迹。这些对象模型然后支持动作模型的创建，从低级运动信号中抽象出来。为了确保信息物理系统的通用性，这些方法只对传感器、效应器和环境的性质做出非常一般的假设。然而，为了提供一个快速评估和改进我们的方法的物理测试平台，该团队设计了一个模型实验室机器人系统，该系统将由现成的组件构建，包括立体相机，平移-倾斜-平移底座和机械臂。为了传播和复制研究结果，核心系统将是负担得起的，并且很容易在其他实验室复制。有计划分发计划，控制软件和实验软件，以鼓励其他实验室复制和扩展工作。同样的系统将作为一个开放式的本科实验室任务，从课堂练习，学期项目，独立的研究项目的平台。有一个非常便宜的网络物理系统模型的初步设计，可以由伺服电机和云台网络摄像头构成，用于合作高中和中学，以传达STEM研究的广度和兴奋。

The physical environment of a cyber-physical system is unboundedly complex, changing continuously in time and space. An embodied cyber-physical system, embedded in the physical world, will receive a high bandwidth stream of sensory information, and may have multiple effectors with continuous control signals. In addition to dynamic change in the world, the properties of the cyber-physical system itself ? its sensors and effectors ? change over time. How can it cope with this complexity? The hypothesis behind this proposal is that a successful cyber-physical system will need to be a learning agent, learning the properties of its sensors, effectors, and environment from its own experience, and adapting over time. Inspired by human developmental learning, the assertion is that foundational concepts such as Space, Object, Action, etc., are essential for such a learning agent to abstract and control the complexity of its world. To bridge the gap between continuous interaction with the physical environment, and discrete symbolic descriptions that support effective planning, the agent will need multiple representations for these foundational domains, linked by abstraction relations. To achieve this, the team is developing the Object Semantic Hierarchy (OSH), which shows how a learning agent can create a hierarchy of representations for objects it interacts with. The OSH shows how the ?object abstraction? factors the uncertainty in the sensor stream into object models and object trajectories. These object models then support the creation of action models, abstracting from low-level motor signals. To ensure generality across cyber-physical systems, these methods make only very generic assumptions about the nature of the sensors, effectors, and environment. However, to provide a physical test bed for rapid evaluation and refinement of our methods, the team has designed a model laboratory robotic system to be built from off-the-shelf components, including a stereo camera, a pan-tilt-translate base, and a manipulator arm. For dissemination and replication of research results, the core system will be affordable and easily duplicated at other labs. There are plans to distribute the plans, the control software, and the software for experiments, to encourage other labs to replicate and extend the work. The same system will serve as a platform for an open-ended set of undergraduate laboratory tasks, ranging from classroom exercises, to term projects, to independent study projects. There is a preliminary design for a very inexpensive version of the model cyberphysical system that can be constructed from servo motors and pan-tilt webcams, for use in collaborating high schools and middle schools, to communicate the breadth and excitement of STEM research.