  This work describes the development of a robotic system that acquires
knowledge incrementally through human interaction where new tools and motions
are taught on the fly. The robotic system developed was one of the five
finalists in the KUKA Innovation Award competition and demonstrated during the
Hanover Messe 2018 in Germany. The main contributions of the system are a) a
novel incremental object learning module - a deep learning based localization
and recognition system - that allows a human to teach new objects to the robot,
b) an intuitive user interface for specifying 3D motion task associated with
the new object, c) a hybrid force-vision control module for performing
compliant motion on an unstructured surface. This paper describes the
implementation and integration of the main modules of the system and summarizes
the lessons learned from the competition.
