Learning Representations for Autonomous Mobile Robotics to Enable Complex Tasks

学习自主移动机器人的表示以实现复杂的任务

• 批准号: RGPIN-2018-04653
• 负责人: Paull, Liam
• 金额: $ 2.04万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712605
• 关键词: Learning; Representations; Autonomous; Mobile; Robotics

We have seen dramatic investments in mobile robotics in recent years, and this is widely considered to be a field of great economic and social promise. The advances of general artificial intelligence (AI), and specifically machine learning (ML) have already yielded significant practical results that are impacting daily life, such as automated face recognition, language translation, and targeted marketing to name a few. However, endowing robots with such capabilities and deploying them in the real world is proving more challenging. But the opportunities for societal impact are significant. Mobile robotics is poised to transform society in the next 20 years in a similar way that industry automation has already in the last 20 years. We have seen limited examples of mobile robotics penetrating the consumer market, most notably applications such as vacuum cleaners and lawn mowers, but massive markets remain open, such as personal transportation, transportation of goods, home assistance and elder care to name a few. One of the core capabilities for any autonomous mobile robot is to be able to perceive the world. This requires amalgamating the stream of sensor data that it is collecting into one coherent and consistent representation. This representation should be rich enough to support the types of tasks that the robot is trying to accomplish. Traditionally, these representations have included little more than geometrical information about the world, and consequently the complexity of the tasks that a robot is capable of achieving have been quite limited. The overall objective of this research program is to develop novel representations that will enable robots to achieve more complex tasks. Recent advances in AI and ML have shown incredible promise. However, the robotics use-case has unique requirements, such as: real-time operation, robustness to incorrect data and failures, scalability, and the ability to understand why a robot took a certain action for safety reasons. Specifically, in this work I will investigate new representations that contain higher level semantic information and statistics about the temporal variability of the world. I will also pay special attention the scalability and real-time requirements of these algorithms as they relate to the available onboard resources. This will be achieved by leveraging deep learning to provide the data-preprocessing in the form of object detections and semantic segmentations. Additionally, the allocation of resources such as computation, memory, and bandwidth, must be done in a way that maximizes the probability of successfully achieving the stated task Robotics has the potential to impact society in a profound way. For this to become a reality requires that robots are safe, robust, and reliable. Achieving these goals requires that robots have a more profound understanding of their surroundings, and that their algorithms are able to scale well.

近年来，我们在移动机器人领域看到了巨大的投资，这被广泛认为是一个具有巨大经济和社会前景的领域。通用人工智能（AI），特别是机器学习（ML）的进步已经产生了影响日常生活的重大实际成果，例如自动人脸识别，语言翻译和目标营销等等。然而，赋予机器人这样的能力并将它们部署到现实世界中更具挑战性。但产生社会影响的机会是巨大的。移动机器人将在未来20年改变社会，就像工业自动化在过去20年里所做的那样。我们已经看到了移动机器人进入消费市场的有限例子，最明显的应用是吸尘器和割草机，但巨大的市场仍然开放，如个人交通、货物运输、家庭辅助和老年人护理等等。