The paper presents an HRI architecture for human‑augmented mapping, which has been implemented and tested on an autonomous mobile robotic platform. Through interaction with a human, the robot can augment its autonomously acquired metric map with qualitative information about locations and objects in the environment. The system implements various interaction strategies observed in independently performed Wizard‑of‑Oz studies. The paper discusses an ontology‑based approach to multi‑layered conceptual spatial mapping that provides a common ground for human‑robot dialogue. This is achieved by combining acquired knowledge with innate conceptual commonsense knowledge in order to infer new knowledge. The architecture bridges the gap between the rich semantic representations of the meaning expressed by verbal utterances on the one hand and the robot’s internal sensor‑based world representation on the other. It is thus possible to establish references to spatial areas in a situated dialogue between a human and a robot about their environment. The resulting conceptual descriptions represent qualitative knowledge about locations in the environment that can serve as a basis for achieving a notion of situational awareness.

The article presents an implemented model for interpreting situated dialogue, as part of a system for human-robot interaction. The underlying assumption is that to understand situated dialogue, communicated meaning needs to be related to the situation(s) it refers to. The model couples incremental processing to a notion of bidirectional connectivity, inspired by how humans process visually situated language. Analyzing an utterance in a word-by-word, left-to-right fashion, a representation of possible utterance interpretations is gradually built up. In a top-down fashion, the model tries to ground these interpretations in models the robot maintains of situations, through which they can prime what is focused on in a situation. In a bottom-up fashion, the (im)possibility to ground certain interpretations primes how the analysis of the utterance further unfolds. The article discusses the implementation of the model in a distributed, cognitive architecture for human-robot interaction, and presents an evaluation on a test suite.

The paper presents an HRI architecture for human-augmented mapping. Through interaction with a hu- man, the robot can augment its autonomously learnt metric map with qualitative information about locations and objects in the environment. The system implements various interaction strategies observed in independent Wizard-of-Oz studies. The paper discusses an ontology-based approach to representing and inferring 2.5-dimensional spatial organization, and presents how knowledge of spatial organization can be acquired au- tonomously or through spoken dialogue interaction.