The work described in this paper addresses learning planning operators by observing expert agents and su-sequent knowledge refinement in a learning-by-doing paradigm. The observations of the expert agent consist of: 1) the sequence of actions being executed, 2) the state in which each action is executed, and 3) the state resulting from the execution of each action. Planning operators are learned from these observation sequences in an incremental fashion utilizing a conservative specific-to-general inductive generalization process. In order to refine the new operators to make them correct and complete, the system uses the new operators to solve practice problems, analyzing and learning from the execution traces of the resulting solutions or execution failures. We describe techniques for planning and plan repair with incorrect and incomplete domain knowledge, and for operator refinement through a process which integrates planning, execution, and plan repair. Our learning method is implemented on top of the PRODIGY architecture and is demonstrated in the extended-strips domain and a subset the process planning domain.