This paper makes two contributions towards enabling a robot to provide explanatory descriptions of its decisions, the underlying knowledge and beliefs, and the experiences that informed these beliefs. First, we present a theory of explanations comprising (i) claims about representing, reasoning with, and learning domain knowledge to support the construction of explanations; (ii) three fundamental axes to characterize explanations; and (iii) a methodology for constructing these explanations. Second, we describe an architecture for robots that implements this theory and supports scalability to complex domains and explanations. We demonstrate the architecture’s capabilities in the context of a simulated robot (a) moving target objects to desired locations or people; or (b) following recipes to bake biscuits.

A fundamental challenge in robotics is to reason with incomplete domain knowledge to explain unexpected observations and partial descriptions extracted from sensor observations. Existing explanation generation systems draw on ideas that can be mapped to a multidimensional space of system characteristics, defined by distinctions, such as how they represent knowledge and if and how they reason with heuristic guidance. Instances in this multidimensional space corresponding to existing systems do not support all of the desired explanation generation capabilities for robots. We seek to address this limitation by thoroughly understanding the range of explanation generation capabilities and the interplay between the distinctions that characterize them. Towards this objective, this paper first specifies three fundamental distinctions that can be used to characterize many existing explanation generation systems. We explore and understand the effects of these distinctions by comparing the capabilities of two systems that differ substantially along these axes, using execution scenarios involving a robot waiter assisting in seating people and delivering orders in a restaurant. The second part of the paper uses this study to argue that the desired explanation generation capabilities corresponding to these three distinctions can mostly be achieved by exploiting the complementary strengths of the two systems that were explored. This is followed by a discussion of the capabilities related to other major distinctions to provide detailed recommendations for developing an explanation generation system for robots.