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In this paper we discuss the problem of action-specific knowledge processing, representation and acquisition by autonomous robots performing everyday activities. We report on a thorough analysis of the household domain, which has been performed on a large corpus of natural-language instructions from the Web and underlines the supreme need of action-specific knowledge for robots acting in those environments. We introduce the concept of Probabilistic Robot Action Cores (PRAC) that are well-suited for encoding such knowledge in a probabilistic first-order knowledge base. We additionally show how such a knowledge base can be acquired by natural language and we address the problems of incompleteness, underspecification and ambiguity of naturalistic action specifications and point out how PRAC models can tackle those.