Human-robot interaction poses tight timing requirements on visual as well as natural language processing in order to allow for natural human-robot interaction. In particular, humans expect robots to incrementally resolve spoken references to visually perceivable objects as the referents are verbally described. We present an integrated robotic architecture with novel incremental vision and natural language processing and demonstrate that incrementally refining attentional focus using linguistic constraints achieves significantly better performance of the vision system compared to non-incremental visual processing.
@inproceedings{cantrelletal12aaaiws,
title={Incremental Referent Grounding with NLP-Biased Visual Search},
author={R. Cantrell and E. Potapova and E. Krause and M. Zillich and M. Scheutz},
year={2012},
booktitle={Proceedings of AAAI 2012 Workshop on Grounding Language for Physical Systems},
url={https://hrilab.tufts.edu/publications/cantrelletal12aaaiws.pdf}
}