In real life environments where robots must deal with complex situations and humans, generalist robots that adapt to novel situations are needed. They are composed by two sub-systems: perception/actuation and knowledge representation, and they need that symbols in the high-level area are coupled to objects and actions of the low-level area. This is the so-called Anchoring Problem. In this paper we present the system we are using to study this problem. It is based on ROSPlan, a framework that provides a generic method for task planning in a ROS system. The high-level area is composed by a planner that uses PDDL files and a knowledge representation system, while the low-level area is defined as a set of robot services exported using ROS actions, services and topics. We plan to contribute to this problem by applying human-robot interaction and learning techniques, and our main objectives are: (1) link an existing symbol with a learned action by interaction, and (2) automated code generation of ad-hoc ROS nodes that connect symbols to specific perceptions/actions.