Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
This addresses the challenge of improving human-robot interaction for users by providing more intuitive and reliable communication, though it appears incremental as it builds on existing cognitive linguistics frameworks.
The paper tackles the problem of enabling robots to understand complex natural language commands by developing a natural language interface that uses Embodied Construction Grammar for deep semantic analysis, allowing robots to resolve fine-grained references and clarify ambiguous commands through verbal interaction.
We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art.