Trustworthy Robotic Assistants

机器人助手的发展由于缺乏连贯和可信的安全框架而受到阻碍。因此，机器人助理应用局限于研究实验室，或者在实践中，与人类的物理交互被故意限制的场景，例如监视，运输或娱乐（例如博物馆）。然而，在家庭/个人领域，交互以非正式，非结构化且通常高度复杂的方式进行。即使在更受限制的工业环境中，对降低制造成本的需求也促使人们创造出具有更大灵活性和智能的机器人。这些机器人需要在人类同事附近工作，接受人类同事的教导，甚至与人类同事进行物理互动。那么，我们如何增强机器人，使它们能够以安全和值得信赖的方式参与与人类的复杂互动？这是一个基本的研究问题，必须解决之前，机器人和人类之间的传统物理安全屏障可以被删除，这是至关重要的近距离人机交互。那么，我们如何才能建立这样的安全论点呢？内部安全机器人必须在各个层面（机械、控制和人机交互）都具有安全性。在较低的机械水平上进行了一些安全工作，以严格限制人类附近的移动，而不考虑这些移动是否“安全”。至关重要的是，还没有人解决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，而且它是否故意或故意做出不安全的动作。这是我们项目的重点。形式验证详尽地分析了机器人的所有可能的选择，但使用了一个大大简化的环境模型。基于仿真的人机交互测试可以快速，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，不考虑真正的人类交互。形成性用户评估正是提供了这种验证，从人类参与者的角度构建了一个全面的分析。我们项目的目的是将这三种方法结合起来，以解决人机交互安全性的整体分析。这将需要大量的研究来增强每一种非常不同的方法，使它们能够协同工作，并随后应用于现实的人机场景。这是以前没有实现过的。在技术之间建立强有力的联系，例如通过正式的断言和交互假设，以及扩展基本技术以科普实际机器人技术，是我们研究的核心部分。虽然实现起来并不容易，但这种组合方法将非常强大。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，这种组合提供了一种新的，强大的，全面的，端到端的验证和确认方法，用于评估人机交互的安全性。

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.