Robotic Data Capture

该提案涉及机器人系统的发展，这些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：机器人摄影纪录片。 我们的目标是开发一个联合收割机，结合摄影记者，婚礼摄影师，科学探险家和法庭速记员的属性。 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，然后到达空间和时间的正确位置进行必要的观察。学习如何做到这一点，并进行正确的观察的一个关键方法是跟踪和遵循具有相同兴趣的人类向导。 这可以通过将该任务分解为几个关键算法组件来完成，如下所述。 该提案侧重于基本的算法问题 这与允许机器人系统记录我们的活动有关，同时与人类密切相关，并以社会适应的方式做出反应。 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，如何通过预测障碍物和移动目标的行为来科普障碍物和移动目标，以及如何适应 照明和物理障碍的变化。 工作的跨度从理论开发开始，然后是模拟，最后部署在一系列真实的机器人车辆上，包括申请人已经在开发的水下车辆。 将使用的技术包括基于传感器的优化、算法博弈论、深度强化学习和反向强化学习。 物理部署目标包括Husky陆地车辆，机器人双体船（船）家族和Aqua水下/两栖车辆，所有这些都已被申请人使用。

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.