  Teaching task-level directives to robots via demonstration is a popular tool
to expand the robot's capabilities to interact with its environment. While
current learning from demonstration systems primarily focuses on abstracting
the task-level knowledge to the robot, these systems lack the ability to
understand which part of the task can be already solved given the robot's prior
knowledge. Therefore, instead of only requiring demonstrations of the missing
pieces, these systems will require a demonstration of the complete task, which
is cumbersome, repetitive, and can discourage people from helping the robot by
performing the demonstrations. Therefore, we propose to use the notion of
"excuses" to identify the smallest change in the robot state that makes a task,
currently not solvable by the robot, solvable -- as a means to solicit more
targeted demonstrations from a human. These excuses are generated automatically
using combinatorial search over possible changes that can be made to the
robot's state and choosing the minimum changes that make it solvable. These
excuses then serve as guidance for the demonstrator who can use it to decide
what to demonstrate to the robot in order to make this requested change
possible, thereby making the original task solvable for the robot without
having to demonstrate it in its entirety. By working with symbolic state
descriptions, the excuses can be directly communicated and intuitively
understood by a human demonstrator. We show empirically and in a user study
that the use of excuses reduces the demonstration time by 54% and leads to a
74% reduction in demonstration size.
