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A robot's ability to provide descriptions of its decisions and beliefspromotes effective collaboration with humans. Providing such transparency isparticularly challenging in integrated robot systems that includeknowledge-based reasoning methods and data-driven learning algorithms. Towardsaddressing this challenge, our architecture couples the complementary strengthsof non-monotonic logical reasoning, deep learning, and decision-tree induction.During reasoning and learning, the architecture enables a robot to provideon-demand relational descriptions of its decisions, beliefs, and the outcomesof hypothetical actions. These capabilities are grounded and evaluated in thecontext of scene understanding tasks and planning tasks performed usingsimulated images and images from a physical robot manipulating tabletopobjects.

Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics