Abstract
Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in the robotic context, thus constitutes taking the correct actions from language instructions. This paper explores the use of cognitive linguistic constructs as well as other constructs such as spatial relationship constructs to configure an NLU system for translating complex natural language instructions into actions to be taken by a robot. This research work illustrates that two important steps are necessary: the first step is to translate a language-dependent surface sentential structure into a language independent deep-level predicate representation, and then the next step is to translate the predicate representation into grounded real-world references and constructs that enable a robot to carry out the language instructions accordingly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taniguchi, A., Taniguchi, T., Cangelosi, A.: Cross-situational learning with Bayesian generative models for multimodal category and word learning in robots. Front. Neurorobotics 11, 66 (2017). https://doi.org/10.3389/fnbot.2017.00066
Matuszek, C., Herbst, E., Zettlemoyer, L., Fox, D.: Learning to parse natural language commands to a robot control system. In: Desai, J., Dudek, G., Khatib, O., Kumar, V. (eds.) Experimental Robotics, pp. 403–415. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00065-7_28
Khayrallah, H., Trott, S., Feldman, J.: Natural language for human robot interaction. In: Proceedings of the Workshop on Human-Robot Teaming at the 10th ACM/IEEE International Conference on Human-Robot Interaction (2015)
Ferrucci, D., et al.: Building Watson: an overview of the DeepQA project. AI Mag. 31(3), 59–79 (2010)
Ganegedara, T.: Natural Language Processing with TensorFlow: Teach Language to Machines Using Python’s Deep Learning Library. Packt Publishing, Birmingham (2018)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Wang, Z., Chong, C.S., Lan, L., Yang, Y., Ho, S.-B., Tong, J.C.: Fine-grained sentiment analysis of social media with emotion sensing. In: IEEE Future Technologies Conference 2016 (FTC 2016), San Francisco, United States, 6–7 December 2016
Winograd, T.: A procedural model of language understanding. In: Schank, R., Colby, K.M. (eds.) Computer Models of Thought and Language. W. H. Freeman & Company, San Francisco (1973)
Ho, S.-B., Wang, Z.: On true language understanding. In: 5th International Conference on AI and Security, New York, 26–28 July 2019
Alomari, M., Duckworth, P., Hogg, D.C., Cohn, A.G.: Natural language acquisition and grounding for embodied robotic systems. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (2017)
Schank, R., Abelson, R.: Scripts, Plans, Goals, and Understanding. Lawrence Erlbaum Associates, Hillsdale (1977)
Pei, M., Jia, Y., Zhu, S.-C.: Parsing video events with goal inference and intent prediction. In: International Conference on Computer Vision. IEEE, New Jersey (2011)
Si, Z., Pei, M., Yao, B., Zhu, S.-C.: Unsupervised learning of AND-OR grammar and semantics from video. In: International Conference on Computer Vision. IEEE, New Jersey (2011)
Cruse, A.: Meaning in Language. Oxford University Press, Oxford (2011)
Langacker, R.W.: Foundation of Cognitive Grammar, vols. I and II. Stanford University Press, Stanford (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ho, SB., Wang, Z. (2019). Language and Robotics: Complex Sentence Understanding. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-27529-7_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27528-0
Online ISBN: 978-3-030-27529-7
eBook Packages: Computer ScienceComputer Science (R0)