This paper describes an integrated architecture for representing,
reasoning with, and interactively learning domain knowledge in the
context of human-robot collaboration. Answer Set Prolog, a
non-monotonic logical reasoning paradigm, is used to represent and
reason with incomplete commonsense domain knowledge, computing a
plan for any given goal and diagnosing unexpected observations.
ASP-based reasoning is also used to guide the interactive learning
of previously unknown actions as well as axioms that encode
affordances, action preconditions, and effects. This learning takes
as input observations from active exploration, reactive action
execution, and human (verbal) descriptions, and the learned actions
and axioms are used for subsequent reasoning. The architecture is
evaluated on a simulated robot assisting humans in an indoor domain.