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In order for cooperative robots ("co-robots") to respond to human behaviorsaccurately and efficiently in human-robot collaboration, interpretation ofhuman actions, awareness of new situations, and appropriate decision making areall crucial abilities for co-robots. For this purpose, the human behaviorsshould be interpreted by co-robots in the same manner as human peers. Toaddress this issue, a novel interpretability indicator is introduced so thatrobot actions are appropriate to the current human behaviors. In addition, thecomplete consideration of all potential situations of a robot's environment isnearly impossible in real-world applications, making it difficult for theco-robot to act appropriately and safely in new scenarios. This is true evenwhen the pretrained model is highly accurate in a known situation. Foreffective and safe teaming with humans, we introduce a new generalizabilityindicator that allows a co-robot to self-reflect and reason about when anobservation falls outside the co-robot's learned model. Based on topic modelingand two novel indicators, we propose a new Self-reflective Risk-awareArtificial Cognitive (SRAC) model. The co-robots are able to consider actionrisks and identify new situations so that better decisions can be made.Experiments both using real-world datasets and on physical robots suggest thatour SRAC model significantly outperforms the traditional methodology andenables better decision making in response to human activities.

Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human Behaviors