  Owing to the recent success of Large Language Models, Modern A.I has been
much focused on linguistic interactions with humans but less focused on
non-linguistic forms of communication between man and machine. In the present
paper, we test how affective-linguistic communication, in combination with
differential outcomes training, affects mutual learning in a human-robot
context. Taking inspiration from child-caregiver dynamics, our human-robot
interaction setup consists of a (simulated) robot attempting to learn how best
to communicate internal, homeostatically-controlled needs; while a human
"caregiver" attempts to learn the correct object to satisfy the robot's present
communicated need. We studied the effects of i) human training type, and ii)
robot reinforcement learning type, to assess mutual learning terminal accuracy
and rate of learning (as measured by the average reward achieved by the robot).
Our results find mutual learning between a human and a robot is significantly
improved with Differential Outcomes Training (DOT) compared to Non-DOT
(control) conditions. We find further improvements when the robot uses an
exploration-exploitation policy selection, compared to purely exploitation
policy selection. These findings have implications for utilizing socially
assistive robots (SAR) in therapeutic contexts, e.g. for cognitive
interventions, and educational applications.
