Participatory Design (PD) in Human-Robot Interaction (HRI) typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. We present LEADOR (Led-by-Experts Automation and Design Of Robots) an end-to-end PD methodology for domain expert co-design, automation and evaluation of social robots. LEADOR starts with typical PD to co-design the interaction specifications and state and action space of the robot. It then replaces traditional offline programming or WoZ by an in-situ, online teaching phase where the domain expert can live-program or teach the robot how to behave while being embedded in the interaction context. We believe that this live teaching can be best achieved by adding a learning component to a WoZ setup, to capture experts' implicit knowledge, as they intuitively respond to the dynamics of the situation. The robot progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in-situ development also means LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived in order to demonstrate the feasibility and worth of this approach, provide concrete examples in its application and identify limitations and opportunities when applying this framework in new environments.
翻译:人类机器人互动(HRI)中的参与式设计(PD)通常仍然局限于早期开发阶段,随后的机器人行为由工程师对机械人的行为进行硬调,然后由工程师对之进行硬调,或用于很少实现自主的奥兹巫师(WoZ)系统。我们介绍了LEADOR(Led-by-Experts A自动化和机器人设计),这是用于社会机器人域专家共同设计、自动化和评估的端到端的PD方法。LEADOR从典型的PD开始,共同设计互动规格以及机器人的状态和行动空间。随后,机器人的行为被传统的离线程序或WoZ(WoZ)的硬调,然后由现场的在线教学阶段取代,由域专家进行实时编程,或教机器人如何在互动背景下进行操作。我们认为,在WoZ设置中增加一个学习部分,以捕捉专家的隐性价值,因为专家对形势的动态反应是直截然的。 机器人在应用中逐渐学习适当的、经专家批准的政策,最终导致完全的自主,甚至在教学、敏感和经实地设计阶段中,在专家系统内部的学习过程中,这种操作过程的推算。