  With growing societal acceptance and increasing cost efficiency due to mass
production, service robots are beginning to cross from the industrial to the
social domain. Currently, customer service robots tend to be digital and
emulate social interactions through on-screen text, but state-of-the-art
research points towards physical robots soon providing customer service in
person. This article explores two possibilities. Firstly, whether transfer
learning can aid in the improvement of customer service chatbots between
business domains. Second, the implementation of a framework for physical robots
for in-person interaction. Modelled on social interaction with Twitter customer
support accounts, transformer-based chatbot models are initially assigned to
learn one domain from an initial random weight distribution. Given shared
vocabulary, each model is then tasked with learning another domain by
transferring knowledge from the previous. Following studies on 19 different
businesses, results show that the majority of models are improved when
transferring weights from at least one other domain, in particular those that
are more data-scarce than others. General language transfer learning occurs, as
well as higher-level transfer of similar domain knowledge, in several cases.
The chatbots are finally implemented on Temi and Pepper robots, with
feasibility issues encountered and solutions are proposed to overcome them.
