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Participatory Design (PD) in Human-Robot Interaction (HRI) typically remainslimited to the early phases of development, with subsequent robot behavioursthen being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systemsthat rarely achieve autonomy. We present LEADOR (Led-by-Experts Automation andDesign Of Robots) an end-to-end PD methodology for domain expert co-design,automation and evaluation of social robots. LEADOR starts with typical PD toco-design the interaction specifications and state and action space of therobot. It then replaces traditional offline programming or WoZ by an in-situ,online teaching phase where the domain expert can live-program or teach therobot how to behave while being embedded in the interaction context. We believethat this live teaching can be best achieved by adding a learning component toa WoZ setup, to capture experts' implicit knowledge, as they intuitivelyrespond to the dynamics of the situation. The robot progressively learns anappropriate, expert-approved policy, ultimately leading to full autonomy, evenin sensitive and/or ill-defined environments. However, LEADOR is agnostic tothe exact technical approach used to facilitate this learning process. Theextensive inclusion of the domain expert(s) in robot design representsestablished responsible innovation practice, lending credibility to the systemboth during the teaching phase and when operating autonomously. The combinationof this expert inclusion with the focus on in-situ development also meansLEADOR supports a mutual shaping approach to social robotics. We draw on twopreviously published, foundational works from which this (generalisable)methodology has been derived in order to demonstrate the feasibility and worthof this approach, provide concrete examples in its application and identifylimitations and opportunities when applying this framework in new environments.

LEADOR: A Method for End-to-End Participatory Design of Autonomous Social Robots