Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot’s planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement.
The role of kinship in knowledge about medicinal plants: evidence for context-dependent model-based biases in cultural transmission?
