Title | ||
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Self-Reflective Risk-Aware Artificial Cognitive Modeling for Robot Response to Human Behaviors. |
Abstract | ||
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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. |
Year | Venue | Field |
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2016 | arXiv: Robotics | Generalizability theory,Interpretability,Simulation,Human behavior,Topic model,Engineering,Cognitive model,Robot,Cognition |
DocType | Volume | Citations |
Journal | abs/1605.04934 | 0 |
PageRank | References | Authors |
0.34 | 29 | 4 |
Name | Order | Citations | PageRank |
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fei han | 1 | 62 | 5.13 |
Christopher Reardon | 2 | 73 | 9.46 |
Lynne E. Parker | 3 | 1233 | 132.54 |
Hao Zhang | 4 | 189 | 23.73 |