  This paper presents a novel human-robot interaction setup for robot and human
learning of symbolic language for identifying robot homeostatic needs. The
robot and human learn to use and respond to the same language symbols that
convey homeostatic needs and the stimuli that satisfy the homeostatic needs,
respectively. We adopted a differential outcomes training (DOT) protocol
whereby the robot provides feedback specific (differential) to its internal
needs (e.g. `hunger') when satisfied by the correct stimulus (e.g. cookie). We
found evidence that DOT can enhance the human's learning efficiency, which in
turn enables more efficient robot language acquisition. The robot used in the
study has a vocabulary similar to that of a human infant in the linguistic
``babbling'' phase. The robot software architecture is built upon a model for
affect-grounded language acquisition where the robot associates vocabulary with
internal needs (hunger, thirst, curiosity) through interactions with the human.
The paper presents the results of an initial pilot study conducted with the
interactive setup, which reveal that the robot's language acquisition achieves
higher convergence rate in the DOT condition compared to the non-DOT control
condition. Additionally, participants reported positive affective experiences,
feeling of being in control, and an empathetic connection with the robot. This
mutual learning (teacher-student learning) approach offers a potential
contribution of facilitating cognitive interventions with DOT (e.g. for people
with dementia) through increased therapy adherence as a result of engaging
humans more in training tasks by taking an active teaching-learning role. The
homeostatic motivational grounding of the robot's language acquisition has
potential to contribute to more ecologically valid and social
(collaborative/nurturing) interactions with robots.
