Abstract
The model of an environment plays a crucial role in autonomous mobile robots, by providing them with the necessary task-relevant information. As robots become more intelligent, they need a richer and more expressive environment model. This model is a map that contains a structured description of the environment that can be used as the robot’s knowledge for several tasks, such as planning and reasoning. In this work, we propose a framework that allows to capture important environment descriptors, such as functionality and ownership of the robot’s surrounding objects, through verbal interaction. Specifically, we propose a corpus of verbal descriptions annotated with frame-like structures. We use the proposed dataset to train two multi-task neural architectures. We compare the two architectures through an experimental evaluation, discussing the design choices. Finally, we describe the creation of a simple interactive interface with our system, implemented through the trained model. The novelties of this work are: (i) the definition of a new problem, i.e., addressing different object descriptors, that plays a crucial role for the robot’s tasks accomplishment; (ii) a specialized corpus to support the creation of rich Semantic Maps; (iii) the design of different neural architectures, and their experimental evaluation over the proposed dataset; (iv) a simple interface for the actual usage of the proposed resources.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bastianelli, E., Castellucci, G., Croce, D., Iocchi, L., Basili, R., Nardi, D.: Huric: a human robot interaction corpus. In: LREC, pp. 4519–4526 (2014)
Dinarelli, M., Quarteroni, S., Tonelli, S., Moschitti, A., Riccardi, G.: Annotating spoken dialogs: from speech segments to dialog acts and frame semantics. In: Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language, pp. 34–41. Association for Computational Linguistics (2009)
Dukes, K.: Semantic annotation of robotic spatial commands. In: Language and Technology Conference (LTC) (2013)
Faruqui, M., Dodge, J., Jauhar, S.K., Dyer, C., Hovy, E., Smith, N.A.: Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166 (2014)
Fillmore, C.J.: Frames and the semantics of understanding. Quad. Semantica 6(2), 222–254 (1985)
Galindo, C., Fernández-Madrigal, J.-A., González, J., Saffiotti, A.: Robot task planning using semantic maps. Robot. Auton. Syst. 56(11), 955–966 (2008)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kollar, T., Tellex, S., Roy, D., Roy, N.: Grounding verbs of motion in natural language commands to robots. In: Khatib, O., Kumar, V., Sukhatme, G. (eds.) Experimental Robotics. STAR, vol. 79, pp. 31–47. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-28572-1_3
Lison, P., Tiedemann, J.: Opensubtitles 2016: extracting large parallel corpora from movie and tv subtitles (2016)
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. STAR, vol. 88, pp. 403–415. (2013). https://doi.org/10.1007/978-3-319-00065-7_28
Pronobis, A., Jensfelt, P.: Large-scale semantic mapping and reasoning with heterogeneous modalities. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 3515–3522. IEEE (2012)
Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: AAAI, pp. 4444–4451 (2017)
Speer, R., Lowry-Duda, J.: Conceptnet at semeval-2017 task 2: extending word embeddings with multilingual relational knowledge. arXiv preprint arXiv:1704.03560 (2017)
Surmann, H., Nüchter, A., Hertzberg, J.: An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Robot. Auton. Syst. 45(3–4), 181–198 (2003)
Vanzo, A., Part, J.L., Yu, Y., Nardi, D., Lemon, O.: Incrementally learning semantic attributes through dialogue interaction. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 865–873. International Foundation for Autonomous Agents and Multiagent Systems (2018)
Walter, M.R., Hemachandra, S., Homberg, B., Tellex, S., Teller, S.: Learning semantic maps from natural language descriptions. Robot. Sci. Syst. (2013)
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
Faridghasemnia, M., Vanzo, A., Nardi, D. (2019). Capturing Frame-Like Object Descriptors in Human Augmented Mapping. In: Alviano, M., Greco, G., Scarcello, F. (eds) AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. Lecture Notes in Computer Science(), vol 11946. Springer, Cham. https://doi.org/10.1007/978-3-030-35166-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-35166-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35165-6
Online ISBN: 978-3-030-35166-3
eBook Packages: Computer ScienceComputer Science (R0)