  Proactive human-robot interaction (HRI) allows the receptionist robots to
actively greet people and offer services based on vision, which has been found
to improve acceptability and customer satisfaction. Existing approaches are
either based on multi-stage decision processes or based on end-to-end decision
models. However, the rule-based approaches require sedulous expert efforts and
only handle minimal pre-defined scenarios. On the other hand, existing works
with end-to-end models are limited to very general greetings or few behavior
patterns (typically less than 10). To address those challenges, we propose a
new end-to-end framework, the TransFormer with Visual Tokens for Human-Robot
Interaction (TFVT-HRI). The proposed framework extracts visual tokens of
relative objects from an RGB camera first. To ensure the correct interpretation
of the scenario, a transformer decision model is then employed to process the
visual tokens, which is augmented with the temporal and spatial information. It
predicts the appropriate action to take in each scenario and identifies the
right target. Our data is collected from an in-service receptionist robot in an
office building, which is then annotated by experts for appropriate proactive
behavior. The action set includes 1000+ diverse patterns by combining language,
emoji expression, and body motions. We compare our model with other SOTA
end-to-end models on both offline test sets and online user experiments in
realistic office building environments to validate this framework. It is
demonstrated that the decision model achieves SOTA performance in action
triggering and selection, resulting in more humanness and intelligence when
compared with the previous reactive reception policies.
