Improving Human Robot Collaboration Using Intention Signalling

[Full物理人机协作是指人类和机器人一起工作以实现共同目标的行为。人类和机器人之间的合作通常与制造业有关，但其他行业的工作人员也很感兴趣。这些包括（但不限于）那些希望提高救援任务，太空旅行和医疗保健效率的人。例如，蛇形机器人被设计用于帮助快速找到被困在瓦砾下的人，机器人手臂被开发用于协助精细的手术。尽管机器人的功能有了很大的发展，但研究表明这些协作系统存在缺陷。一个突出的问题是，尽管机器可以对周围的物体和事件进行分类，但底层的过程可能是不正确的。举例来说：一台机器可能能够正确地对狗和狼的图像进行分类-但它可能是基于背景中的雪进行分类的情况下，我们不会发现，直到犯了一个错误。在机器分类狗与狼的图像的情况下，错误的成本很低，但在人机协作中，这种分类错误的成本可能导致资源损失（时间，精力和金钱），甚至对最终用户造成伤害。为了提高人机协作的效率，避免代价高昂的错误，有人建议，那些致力于设计和建造系统的人与那些具有社会科学专业知识的人之间必须进行协作。具体来说，由于数据的复杂性和人类行为的复杂性，有人建议认知科学、心理学和机器人学的专家之间应该进行合作。这项提议源于这样一个组合--1）社会大脑行动实验室（Social Brain in Action Laboratory）（一家专门研究心理学和认知神经科学的英国实验室），以及2）反思互动，协作和参与实验室（加拿大一家专门研究计算机科学和机器人设计的实验室）。尽管做出了许多努力来推导出能够清楚地解释其决策原理的系统，事实证明，它极具挑战性和耗时（Gilpin，2018; Rudin，2019）。我们提出了一种替代的解决方案，尽管有可能不正确的分类，可以阻止错误发生的错误。具体来说，我们认为，这是机器人的行动，导致错误，而不是决定本身。因此，我们建议在行动之前，机器人应该向用户指示它要做什么。在看到这个提示时，用户可以允许移动继续，或者如果即将出错，则停止机器。除了提高协作的效率，我们预计，对机器人的信任，以及互动的舒适性，也将通过添加“意图信号”得到改善。有大量来自心理科学和机器人技术的文献表明，当所有成员的意图都是已知的时，团队的工作效率更高，因此我们相信添加此功能将提高交互的效率。为了研究机器人“意图信号”是否会提高任务效率，改善用户对机器人的感知（例如喜欢，信任.），并提高用户对协作的感知（例如，享受、舒适性），我们打算进行实验。在实验中，我们建议进行一个桌面实验，参与者将与机器人手臂（无论是信号或沉默）完成一个难题。多伦多大学的这项技术有能力处理许多信号，因此研究人员打算汇集他们的知识，并决定在这项实验中应该使用哪种信号-例如，凝视提示，手势，或者数字桌面上的可视化。

[Full referenced proposal in 'Proposal' document]Physical human-robot collaboration refers to the act of a human and a robot working together to achieve a common goal. Collaboration between humans and robots is generally associated with the manufacturing industry, however there is also much interest from those working in other sectors too. These include (but are not limited to) those hoping to improve the efficiency of rescue missions, space travel, and healthcare. For example, snake-like robots have been designed to help quickly find people trapped under rubble, and robotic arms have been developed to assist during delicate surgeries. Despite large developments in the functionality of robots, studies have indicated flaws in these collaborative systems. One prominent issue is that although machines can classify objects and events in its surroundings, the underlying processes can be incorrect. For example: a machine may be able to correctly classify images of dogs vs wolves - but it could be the case that it is classifying based on the snow in the background, and we wouldn't find out until a mistake was made. In the case of the machine sorting images of dogs vs wolves the cost of a mistake is low, but in human-robot collaboration the cost of such a classification error could lead to lost resources (time, effort, and money), or even injury to the end-user. To improve the efficiency of human-robot collaborations, and avoid costly errors, it has been suggested that there must be collaboration between those working to design and build the systems, and those with expertise in social sciences. Specifically, due to the complexity of the data and the intricacies of human behaviour, it has been suggested that there should be collaboration between experts in Cognitive Science, Psychology, and Robotics. This proposal stems from such a combination - a collaboration between 1) the Social Brain in Action Laboratory (a UK laboratory specialising in Psychology and Cognitive Neuroscience), and 2) the Rethinking Interaction, Collaboration and Engagement Laboratory (a Canadian Laboratory specialising in Computer Science and Robot Design).Despite much effort to derive systems which clearly explain the rationale for their decisions, it is proving extremely challenging and time-consuming (Gilpin, 2018; Rudin, 2019). We propose an alternative solution which, despite the possibility of incorrect classifications, could stop errors from errors occuring. Specifically, we argue that it is the action of the robot which leads to the error, not the decision itself. As a result, we propose that before acting, the robot should indicate to the user what it is going to do. Upon seeing this cue the user can allow the movement to continue, or halt the machine if a mistake is about to be made. In addition to increasing the efficiency of the collaboration, we anticipate that trust towards the robot, and comfortability in the interaction, will also be improved by adding 'intention signalling'. There is a wealth of literature from both psychological sciences and robotics which suggests that teams work more effectively when intentions of all members are known, so we are confident that adding this feature will increase the efficiency of the interaction.To investigate whether robot 'intention signalling' increases task efficiency, improves user perceptions of the robot (e.g. liking, trust...), and improves user perceptions of the collaboration (e.g. enjoyment, comfortability), we intend to conduct an experiment. In the experiment we propose to conduct a table-top experiment in which participants will work with a robotic arm (either signalling or acting silently) to complete a puzzle. The technology at the University of Toronto has the capability to do numerous signals, so the research intend to pool their knowledge and decide which signal should be used in this experiment - e.g. a gaze cue, a gesture, or perhaps a visualisation on a digital tabletop.