We present a system for generating and understanding of dynamic and static spatial relations in robotic interaction setups. Robots describe an environment of moving blocks using English phrases that include spatial relations such as "across" and "in front of". We evaluate the system in robot-robot interactions and show that the system can robustly deal with visual perception errors, language omissions and ungrammatical utterances.
This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands. n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our speciﬁc approach is to develop models that factor according to the linguistic structure of a command. We ﬁrst describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation. We present a methodology for enabling service robots to follow natural language commands from non-expert users, with and without user-specified constraints, with a particular focus on spatial language understanding. As part of our approach, we propose a novel extension to the semantic field model of spatial prepositions that enables the representation of dynamic spatial relations involving paths. The design, system modules, and implementation details of our robot software architecture are presented and the relevance of the proposed methodology to interactive instruction and task modification through the addition of constraints is discussed. The paper concludes with an evaluation of our robot software architecture implemented on a simulated mobile robot operating in both a 2D home environment and in real world environment maps to demonstrate the generalizability and usefulness of our approach in real world applications.