Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics
This addresses the problem of human-robot collaboration by enhancing transparency, though it appears incremental as it integrates existing methods.
The paper tackles the challenge of providing transparency in integrated robot systems by combining non-monotonic logical reasoning, deep learning, and decision-tree induction to enable robots to describe their decisions and beliefs on-demand, with evaluation in scene understanding and planning tasks using simulated and physical robot images.
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.