Group Travel Award for 2017 Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory, and Efficiency

2017年自主飞行学习感知与控制研讨会团体旅游奖：安全、记忆和效率

• 批准号: 1743262
• 负责人: Konstantinos Karydis
• 金额: $ 1.2万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1743262&HistoricalAwards=false
• 关键词: Group; Travel; Award; 2017; Workshop

Aerial robots, commonly referred to as drones, offer promise in several research, educational, defense and commercial applications. Some examples include precise agriculture, aerial photography, agile inspection and monitoring, and package delivery. In most of those applications that aerial robots have started venturing outside the research lab and into the real world, robot operation is often semi-autonomous. Semi-autonomous operation typically assumes availability of GPS signal for localization, and at least some prior information about the working environment. Sensory-based, fully autonomous operation in unknown environments remains mostly at the research stage. Yet, endowing full autonomy to aerial robots can enhance their impact on the nation's education, economy, and defense. To this end, it is important to seamlessly merge perception, planning, and control for autonomous robotic flight in unknown environments. This can be achieved by integrating machine learning tools into aerial robot perception and control. Deep learning has recently emerged as a promising way to extract semantic meaning for autonomy. Learning perception and control for autonomous flight can be approached by replacing hand-engineered map representations with raw sensor observations, and learning appropriate responses. However, this is not a straightforward task, and several challenges remain. This workshop critically addresses how to i) best incorporate memory and ii) derive safety guarantees for the learning-based system. These two aspects are necessary to improve the capacity of aerial robots to operate autonomously in unknown environments, and to push forward the current state-of-the-art in robotic flight. In addition to the domain of robotic flight, the outcomes of this workshop are relevant to endowing autonomy in general robotic systems that are able to learn, thus helping make autonomous robots ubiquitous.The objective of this workshop is to address the theoretical and technical challenges faced in order to endow learning-based systems with the capacity to operate autonomously in unknown environments. A critical step in this effort is to understand how memory-augmented autonomous learners can operate with provable safety guarantees. The workshop thus examines two highly-relevant questions. i) How to theoretically analyze the data and structure of learning-based systems to provide guarantees on safety and task success? ii) What is the effect of long-term memory and, in particular, can recurrent connections or dynamic external memory replace global map information? The workshop seeks answers to these questions by bringing together experts from robot planning and control, reinforcement learning and deep learning, and formal methods. The workshop also solicits participation of contributed authors working in relevant areas. These include but are not limited to applying deep reinforcement learning for vision-based control of underactuated robots; learning visuomotor policies and deriving formal guarantees for learning based on neural networks; and developing neural network architectures that involve temporal recurrence and memory. The above questions are asked here in the context of high-speed aerial robot autonomous navigation. However, their scope can be generalized to other areas of robotics that learning perception and control for autonomous operation in unknown environments is desirable; examples include manipulation and legged locomotion.

通常称为无人机的空中机器人在几项研究，教育，国防和商业应用中提供了承诺。 一些例子包括精确的农业，航空摄影，敏捷检查和监视以及包装交付。 在大多数航空机器人开始在研究实验室外进行冒险并进入现实世界的应用中，机器人操作通常是半自治的。 半自治操作通常假设GPS信号用于定位，以及至少一些有关工作环境的先前信息。在未知环境中，基于感觉的，完全自主的操作仍在研究阶段。 然而，赋予空中机器人的充分自治可以增强其对国家教育，经济和国防的影响。 为此，在未知环境中无缝合并自主机器人飞行的感知，计划和控制很重要。 这可以通过将机器学习工具集成到空中机器人的感知和控制中来实现。 最近，深度学习已成为提取自治语义含义的一种有希望的方法。 可以通过用原始传感器观察替换手工设计的地图表示并学习适当的响应来接触自主飞行的学习感知和控制。 但是，这并不是一项简单的任务，仍然存在一些挑战。 该研讨会批判性地解决了如何）最好地纳入内存，ii）为基于学习的系统提供安全保证。 这两个方面对于提高空中机器人在未知环境中自动运行的能力以及推动机器人飞行中最新的前进是必要的。 除了机器人飞行的领域外，该研讨会的结果与能够学习的通用机器人系统中的赋予自主权有关，从而有助于使自主机器人无处不在。该研讨会的目的是解决以赋予基于自主环境的能力在不知名的环境中运行能力的理论和技术挑战。 这项工作的关键步骤是了解如何获得可证明的安全保证的自主学习者如何运作。 因此，研讨会研究了两个高度相关的问题。 i）理论上如何分析基于学习的系统的数据和结构以确保安全和任务成功？ ii）长期内存，尤其是复发连接或动态外部内存的影响是什么？ 研讨会通过将机器人计划和控制，强化学习和深度学习以及正式方法的专家聚集在一起来寻求这些问题的答案。该研讨会还招募了在相关领域工作的贡献者的参与。 这些包括但不限于将深入的增强学习应用于对机器人不足的基于视力的控制；学习视觉运动政策并根据神经网络获得正式保证；并开发涉及时间复发和记忆的神经网络体系结构。 以上问题是在高速航空机器人自动导航的背景下提出的。 但是，他们的范围可以推广到其他机器人技术领域，这些机器人技术在未知环境中学习感知和控制自主操作是可取的。例子包括操纵和腿部运动。