The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for artificial intelligence (AI) planning. This paper describes Learning Object-Centred Models (LOCM), a system that carries out the automated generation of a planning domain model from example training plans. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits assumptions about the kinds of domain model it has to generate, rather than handcrafted clues or planner-oriented knowledge. It assumes that actions change the state of objects, and require objects to be in a certain state before they can be executed. In this paper, we describe the implemented LOCM algorithm, the assumptions that it is based on, and an evaluation using plans generated through goal-directed solutions, through random walk, and through logging human-generated plans for the game of freecell. We analyze the performance of LOCM by its application to the induction of domain models from five domains.