Going Beyond Literal Command-Based Instructions: Extending Robotic Natural Language Interaction Capabilities

The ultimate goal of human natural language interaction is to communicate intentions. However, these intentions are often not directly derivable from the semantics of an utterance (e.g., when linguistic modulations are employed to convey politeness, respect, and social standing). Robotic architectures with simple command-based natural language capabilities are thus not equipped to handle more liberal, yet natural uses of linguistic communicative exchanges. In this paper, we propose novel mechanisms for inferring intentions from utterances and generating clarification requests that will allow robots to cope with a much wider range of task-based natural language interactions. We demonstrate the potential of these inference algorithms for natural humanrobot interactions by running them as part of an integrated cognitive robotic architecture on a mobile robot in a dialoguebased instruction task.