Robotic Data Capture

这项提议涉及到机器人系统的发展，这种系统可以在我们的世界中移动，并记录我们感兴趣的事件和现象：机器人照片纪录片。目标是开发结合摄影记者、婚礼摄影师、科学探险家和法庭速记员的系统。所有这些问题的关键是意识到哪些现象是人类观察者感兴趣的，然后在空间和时间上到达正确的地方进行必要的观察。既要学习如何做到这一点，又要做出正确的观察，一个关键的方法就是跟踪和跟踪有相同兴趣的人类向导。这可以通过将该任务分解成几个关键的算法组件来完成，如下所述。 该提案集中在基本的算法问题上 与允许机器人系统记录我们的活动有关，同时在靠近人类的情况下工作，并以适应社会的方式做出反应。这包括知道观察什么，何时进行观察，如何以社会可接受的方式进行观察，如何通过预测障碍和移动目标的行为来应对障碍和移动目标，以及如何适应 照明和物理障碍的变化。这项工作的跨度从理论发展开始，然后是仿真，最后是在一系列真实的机器人上部署，包括申请人已经在开发的水下机器人。 将使用的技术跨越了基于传感器的优化、算法博弈论、深度强化学习和逆强化学习。实际部署目标包括一辆赫斯基陆地车辆、一系列机器人双体船(船)和Aqua水下/两栖车辆，所有这些都已被申请者使用。

This proposal addresses the development of robotic systems that can move about in our world and record events and phenomena of interest to us: the robotic photo-documentarian. The objective is to develop systems that combine the attributes of photo-journalist, wedding photographer, scientific explorer, and court stenographer. The key to all these problems is an awareness of which phenomena are of interest to human obervers, and then getting to the right places in space and time to make the requisite observations. One key way to both learning how to do that, and making the right observations is to track and follow human guides who have the same interests. This can be done be decomposing this task into a few key algorithmic components, as described below. The proposal focusses on basic algorithmic issues associated with allowing robotic systems to document our activities while working in close proximity to humans and responding in a socially-adaptive manner. This includes known what to observe, when to make those observations, how to do so in a socially acceptable manner, and how to cope with obstacles and moving targets by predicting their behaviour, and how to adapt to changes in lighting and physical impediments. The span of the work begins with theoretical development, then simulation, and finally deployment on a range of real robotic vehicles including underwater vehicles already under development by the applicant. The techniques to be used span sensor-based optimization, algorithmic game theory, deep-reinforcement learning and inverse reinforcement learning. The physical deployment targets include a Husky terrestrial vehicle, a family of robotic catamarans (boats), and the Aqua underwater/amphibious vehicle all of which are already in use by the applicant.