Improving Trust-Guided Behavior Adaptation Using Operator Feedback

It is important for robots to be trusted by their human teammates so that they are used to their full potential. This paper focuses on robots that can estimate their own trustworthiness based on their performance and adapt their behavior to engender trust. Ideally, a robot can receive feedback about its performance from teammates. However, that feedback can be sporadic or non-existent (e.g., if teammates are busy with their own duties), or come in a variety of forms (e.g., different teammates using different vocabularies). We describe a case-based algorithm that allows a robot to learn a model of feedback and use that model to adapt its behavior. We evaluate our system in a simulated robotics domain by showing that a robot can learn a model of operator feedback and use that model to improve behavior adaptation.