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

A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training