  In order for cooperative robots ("co-robots") to respond to human behaviors
accurately and efficiently in human-robot collaboration, interpretation of
human actions, awareness of new situations, and appropriate decision making are
all crucial abilities for co-robots. For this purpose, the human behaviors
should be interpreted by co-robots in the same manner as human peers. To
address this issue, a novel interpretability indicator is introduced so that
robot actions are appropriate to the current human behaviors. In addition, the
complete consideration of all potential situations of a robot's environment is
nearly impossible in real-world applications, making it difficult for the
co-robot to act appropriately and safely in new scenarios. This is true even
when the pretrained model is highly accurate in a known situation. For
effective and safe teaming with humans, we introduce a new generalizability
indicator that allows a co-robot to self-reflect and reason about when an
observation falls outside the co-robot's learned model. Based on topic modeling
and two novel indicators, we propose a new Self-reflective Risk-aware
Artificial Cognitive (SRAC) model. The co-robots are able to consider action
risks and identify new situations so that better decisions can be made.
Experiments both using real-world datasets and on physical robots suggest that
our SRAC model significantly outperforms the traditional methodology and
enables better decision making in response to human activities.
