A Review on Learning Planning Action Models for Socio-Communicative HRI
  For social robots to be brought more into widespread use in the fields of
companionship, care taking and domestic help, they must be capable of
demonstrating social intelligence. In order to be acceptable, they must exhibit
socio-communicative skills. Classic approaches to program HRI from observed
human-human interactions fails to capture the subtlety of multimodal
interactions as well as the key structural differences between robots and
humans. The former arises due to a difficulty in quantifying and coding
multimodal behaviours, while the latter due to a difference of the degrees of
liberty between a robot and a human. However, the notion of reverse engineering
from multimodal HRI traces to learn the underlying behavioral blueprint of the
robot given multimodal traces seems an option worth exploring. With this
spirit, the entire HRI can be seen as a sequence of exchanges of speech acts
between the robot and human, each act treated as an action, bearing in mind
that the entire sequence is goal-driven. Thus, this entire interaction can be
treated as a sequence of actions propelling the interaction from its initial to
goal state, also known as a plan in the domain of AI planning. In the same
domain, this action sequence that stems from plan execution can be represented
as a trace. AI techniques, such as machine learning, can be used to learn
behavioral models (also known as symbolic action models in AI), intended to be
reusable for AI planning, from the aforementioned multimodal traces. This
article reviews recent machine learning techniques for learning planning action
models which can be applied to the field of HRI with the intent of rendering
robots as socio-communicative.
