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Trustworthy Robotic Assistants

值得信赖的机器人助手

基本信息

批准号：
EP/K006193/1
负责人：
Michael Fisher
金额：
$ 48.28万
依托单位：
University of Liverpool
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FK006193%2F1
关键词：
Trustworthy Robotic Assistants

项目摘要

The development of robotic assistants is being held back by the lack of a coherent and credible safety framework. Consequently, robotic assistant applications are confined either to research labs or, in practice, to scenarios where physical interaction with humans is purposely limited, e.g. surveillance, transport or entertainment (e.g. museums).In the domestic/personal domain, however, interactions take place in an informal, unstructured, and typically highly complex way. Even in more constrained industrial settings, the need for reduced manufacturing costs is motivating the creation of robots capable of much greater flexibility and intelligence. These robots need to work near to, be taught by, and perhaps even interact physically with, human co-workers.So, how can we enhance robots so that they can participate in sophisticated interactions with humans in a safe and trustworthy manner? This is a fundamental research question that must be addressed before the traditional physical safety barrier between the robot and the human can be removed, which is essential for close-proximity human-robot interactions. How, then, might we establish such safety arguments? Intrinsically safe robots must incorporate safety at all levels (mechanical; control; and human interaction). There has been some work on safety at lower, mechanical, levels to severely restrict movements near humans, without regard to whether the movements are "safe" or not. Crucially, no one has yet tackled the high-level behaviours of robotic assistants during interaction with humans, i.e. not only whether the robot makes safe moves, but whether it knowingly or deliberately makes unsafe moves. This is the focus of our project.Formal verification exhaustively analyses all of the robot's possible choices, but uses a vastly simplified environmental model. Simulation-based testing of robot-human interactions can be carried out in a fast, directed way and involves a much more realistic environmental model, but is essentially selective and does not take into account true human interaction. Formative user evaluation provides exactly this validation, constructing a comprehensive analysis from the human participant's point of view.It is the aim of our project to bring these three approaches together to tackle the holistic analysis of safety in human-robot interactions. This will require significant research in enhancing each of the, very distinct, approaches so they can work together and subsequently be applied in realistic human-robot scenarios. This has not previously been achieved. Developing strong links between the techniques, for example through formal assertions and interaction hypotheses, together with extension of the basic techniques to cope with practical robotics, is the core part of our research.Though non-trivial to achieve, this combined approach will be very powerful. Not only will analysis from one technique stimulate new explorations for the others, but each distinct technique actually remedies some of the deficiencies of another. Thus, this combination provides a new, strong, comprehensive, end-to-end verification and validation method for assessing safety in human-robot interactions.

由于缺乏连贯可信的安全框架，机器人助手的发展正受到阻碍。因此，机器人助手的应用被限制在研究实验室或在实践中，故意限制与人类的物理交互的场景，例如监视、交通或娱乐(例如博物馆)。然而，在家庭/个人领域，交互以非正式的、非结构化的且通常非常复杂的方式发生。即使在更受限制的工业环境中，对降低制造成本的需求也在推动机器人的创造，这些机器人能够更灵活、更智能。这些机器人需要在人类同事附近工作，接受他们的教导，甚至与他们进行身体上的互动。那么，我们如何改进机器人，使它们能够以安全可靠的方式参与到与人类的复杂互动中？这是一个必须解决的基础性研究问题，才能消除机器人和人类之间传统的物理安全屏障，这是人与机器人近距离交互的关键。那么，我们如何才能确立这样的安全论点呢？本质上安全的机器人必须包括所有级别的安全(机械、控制和人类交互)。已经在较低的机械水平上进行了一些安全方面的工作，以严格限制靠近人类的活动，而不考虑这些活动是否“安全”。至关重要的是，还没有人研究机器人助手在与人类互动时的高级行为，即不仅机器人是否做出安全的动作，而且它是故意还是故意做出不安全的动作。这是我们项目的重点。正式验证详尽地分析了机器人的所有可能选择，但使用了一个大大简化的环境模型。基于模拟的机器人-人类交互测试可以快速、直接的方式进行，并涉及更现实的环境模型，但本质上是选择性的，没有考虑真正的人类交互。形成性用户评价提供了这种验证，从人类参与者的角度构建了一个全面的分析。我们项目的目标是将这三种方法结合在一起，解决人-机器人交互中的整体安全分析。这将需要大量的研究来增强每一种非常不同的方法，以便它们能够协同工作，并随后应用于现实的人类-机器人场景。这是以前从未实现过的。发展这些技术之间的强大联系，例如通过形式断言和相互作用假设，以及扩展基本技术以应对实际的机器人，是我们研究的核心部分。尽管实现起来并不是微不足道的，但这种组合方法将非常强大。来自一种技术的分析不仅会激发对其他技术的新探索，而且每一种不同的技术实际上都弥补了另一种技术的一些不足。因此，这种结合为评估人-机器人交互的安全性提供了一种新的、强大的、全面的、端到端的验证和验证方法。