Recent years show the development of large scale resources (e.g. FrameNet for the Frame Semantics) that supported the definition of several state-of-the-art approaches in Natural Language Processing. However, the reuse of existing resources in heterogeneous domains such as Human Robot Interaction is not straightforward. The generalization offered by many data driven methods is strongly biased by the employed data, whose performance in out-of-domain conditions exhibit large drops. In this paper, we present the Human Robot Interaction Corpus (HuRIC). It is made of audio files paired with their transcriptions referring to commands for a robot, e.g. in a home environment. The recorded sentences are annotated with different kinds of linguistic information, ranging from morphological and syntactic information to rich semantic information, according to the Frame Semantics, to characterize robot actions, and Spatial Semantics, to capture the robot environment. All texts are represented through the Abstract Meaning Representation, to adopt a simple but expressive representation of commands, that can be easily translated into the internal representation of the robot.