  In recent years, robots are used in an increasing variety of tasks,
especially by small- and medium- sized enterprises. These tasks are usually
fast-changing, they have a collaborative scenario and happen in unpredictable
environments with possible ambiguities. It is important to have methods capable
of generating robot programs easily, that are made as general as possible by
handling uncertainties. We present a system that integrates a method to learn
Behavior Trees (BTs) from demonstration for pick and place tasks, with a
framework that uses verbal interaction to ask follow-up clarification questions
to resolve ambiguities. During the execution of a task, the system asks for
user input when there is need to disambiguate an object in the scene, when the
targets of the task are objects of a same type that are present in multiple
instances. The integrated system is demonstrated on different scenarios of a
pick and place task, with increasing level of ambiguities. The code used for
this paper is made publicly available.
