Incremental knowledge acquisition for human-robot collaboration

Human-robot collaboration in practical domains typically requires considerable domain knowledge and labeled examples of objects and events of interest. Robots frequently face unforeseen situations in such domains, and it may be difficult to provide labeled samples. Active learning algorithms have been developed to allow robots to ask questions and acquire relevant information when necessary. However, human participants may lack the time and expertise to provide comprehensive feedback. The incremental active learning architecture described in this paper addresses these challenges by posing questions with the objective of maximizing the potential utility of the response from humans who lack domain expertise. Candidate questions are generated using contextual cues, and ranked using a measure of utility that is based on measures of information gain, ambiguity and human confusion. The top-ranked questions are used to update the robot's knowledge by soliciting answers from human participants. The architecture's capabilities are evaluated in a simulated domain, demonstrating a significant reduction in the number of questions posed in comparison with algorithms that use the individual measures or select questions randomly from the set of candidate questions.