Examining the Interactions and Dependencies between Active Vision and Reasoning

机器学习方法的成功，例如深度学习神经网络中的成功，已经改变了计算视觉和人工智能（AI）。虽然令人兴奋，但在机器人环境中，这些方法如何应用似乎存在不确定性。在2015年机器人与自动化国际会议（ICRA）上，有两个演讲强调了这一点。D.罗斯（麻省理工学院）的全体会议主题演讲强调，“机器人不能解决问题”：它们不能对自己的环境和任务进行推理。G. Hager（约翰霍普金斯大学）介绍了他的机器人视觉研讨会，强调了机器人传感系统利用注意力和上下文的必要性，这是“弄清楚事情”的关键要素。也许还需要更多的东西。 我的研究计划的长期目标是使用计算语言来开发和形式化具有预测能力的人类视觉和视觉认知理论，从而促进视觉科学，并使用这些来通知实际机器人和机器视觉系统的工程（而不一定是模仿）。在这种情况下，目前的建议侧重于如何主动视觉和推理策略可以耦合，以开发有效的主动推理方法。我们的研究计划提出将推理机制的需求与主动感知的能力联系起来，并从机器视觉的角度出发，对人类视觉神经生物学的现代理解具有强烈的动机。这是我们的假设，视觉算法必须是可调的输入，环境和任务的时刻，在一个动态的方式，为了使系统能够捕捉到的一般性，在人类视觉。我们的目标是开发计算机视觉算法并展示它们的性能，测试这个基本假设，但要基于家庭、老年人和体弱者的自主伴侣机器人的机器人感知需求。 先进的机器人研究对加拿大的重要性很容易证明; NSERC已经投资了现场机器人战略网络，我是其中的一个联合PI（PI：G。Dudek）。该网络既涉及机器人技术的基础研究，也涉及各种领域的应用，包括陆地，空中，水上机器人，用于自然资源，健康，安全等。即使付出了这么大的努力，机器人和机器人感知的某些方面仍然没有涵盖;本提案解决了其中一个需求。机器人应该了解他们的环境和情况，能够推理如何执行任务，他们的行动的效果和影响，并能够确定他们的行动何时可能不正确，并制定更好的计划来完成任务。这些似乎不仅对于实现自然资源和制造业应用的鲁棒性至关重要，而且对于家庭和老年人的伴侣机器人的安全性和人类行为也至关重要。

The successes of machine learning methods, such as those found in Deep Learning Neural Networks, have transformed computational vision and Artificial Intelligence (AI). Although exciting, in a robotic context, there seems to be uncertainty about how these methods apply. At the 2015 Int. Conf. on Robotics and Automation (ICRA), two presentations emphasize this. D. Rus's (MIT) plenary keynote stressed that "robots can't figure things out": they cannot reason about their circumstances and tasks. G. Hager (Johns Hopkins U), introducing his Workshop on Robotic Vision, highlighted the need for robotic sensing systems to exploit attention and context, key elements for "figuring things out". Perhaps something more is needed. The long-term goal of my research program is to use the language of computation to develop and formalize theories of human vision and visual cognition that have predictive power, thus furthering vision science, and use these to inform the engineering (and not necessarily mimic) of practical robot and machine vision systems. Within this context, the current proposal focuses on how active vision and reasoning strategies may be coupled in order to develop effective active reasoning methods. Our research plan proposes bridging the needs of inference mechanisms to the capabilities of active perception, with a perspective on machine vision that is strongly motivated by a modern understanding of human visual neurobiology. It is our hypothesis that the visual algorithms must be tunable to the input, environment and task of the moment, in a dynamic fashion, in order to enable a system to capture the generality seen in human vision. The goal is to develop computer vision algorithms and demonstrate their performance, testing this basic hypothesis, but being grounded in the robotic perception needs of autonomous companion robots for home, for the elderly, and for the infirm. The importance of advanced robotics research to Canada is easy to demonstrate; NSERC has invested in the Strategic Network for Field Robotics, of which I am a co-PI (PI: G. Dudek). That network addresses both basic research into robotics as well as applications across a wide variety of domains covering terrestrial, airborne, water-based robots for natural resources, health, security and more. Even with this large effort, there are aspects of robotics and robotic perception not covered; this proposal addresses one of those needs. Robots should understand their environment and circumstances, be able to reason about how to perform their tasks, the effects and impacts of their actions, and be able to determine when their actions may not be correct and develop better plans for achieving their tasks. These seem critical not only for achieving robustness in applications in natural resources and manufacturing, but also safety and human-like behaviour for companion robots in the home and for the elderly.