  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.
