  A robot's ability to provide descriptions of its decisions and beliefs
promotes effective collaboration with humans. Providing such transparency is
particularly challenging in integrated robot systems that include
knowledge-based reasoning methods and data-driven learning algorithms. Towards
addressing this challenge, our architecture couples the complementary strengths
of non-monotonic logical reasoning, deep learning, and decision-tree induction.
During reasoning and learning, the architecture enables a robot to provide
on-demand relational descriptions of its decisions, beliefs, and the outcomes
of hypothetical actions. These capabilities are grounded and evaluated in the
context of scene understanding tasks and planning tasks performed using
simulated images and images from a physical robot manipulating tabletop
objects.
