Robust Natural Language Processing - Combining Reasoning, Cognitive Semantics and Construction Grammar for Spatial Language
This addresses the challenge of robust natural language processing for spatial relations in robotics, though it appears incremental as it combines existing reasoning and grammar approaches.
The paper tackled the problem of generating and understanding spatial language in robotic interactions, and the result was a system that robustly handled visual perception errors, language omissions, and ungrammatical utterances in robot-robot setups.
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.