Human-Robot Interaction


Robots are increasinlgy being marketed for consumer applications (e.g. cleaning or entertainment). A natural language interaction is more and more expected to make them more appealing and accessible to the end user. Latest advances in speech technologies enable different and richer levels of interaction. In Human Robot Interaction, command understanding is the baseline for a natural interaction. More complex interactions require different levels of natural language processing capabilities, such as dialogue management.

In our group we deal with the Natural Language Understanding for Robotic platforms. In this area we investigate how state-of-the-art textual inference technologies can be employed and adapted for HRI systems. The final aim of this research is to propose a unifying framework able to capture semantic aspects as these are needed in the HRI area. We foster the idea that many problems tackled and solved in Natural Language Processing, e.g. Semantic Role Labeling (SRL) (Palmer et al., 2010), can be taken into account for HRI.


Roberto Basili, Emanuele Bastianelli, Giuseppe Castellucci, Danilo Croce

Related Projects

HuRIC: a Human Robot Interaction Corpus.


D.L.Chen,R.J. Mooney,Learning to interpret natural language navigation instructions from observations,in Proceedings of the 25th AAAI Conference on AI, 2011, pp. 859–865.

G.J.M. Kruijff, H.Zender, P.Jensfelt, H.I.Christensen, Situated dialogue and spatial organisation: What, where and why?, International Journal of Advanced Robotic Systems 4 (2).

J.Bos, T.Oka, A spoken language interface with a mobile robot, Artificial Life and Robotics 11(1) (2007)42

SAG Publications

Andrea Vanzo, Danilo Croce, Emanuele Bastianelli, Roberto Basili, Daniele Nardi (2020): Grounded language interpretation of robotic commands through structured learning. In: Artificial Intelligence Volume 278, January 2020, 103181, 278, 2020.

Emanuele Bastianelli and Giuseppe Castellucci and Danilo Croce and Roberto Basili and Daniele Nardi (2017): Structured learning for spoken language understanding in human-robot interaction. In: International Journal of Robotics Research, 36 (5-7), pp. 660–683, 2017.

Emanuele Bastianelli, Giuseppe Castellucci, Danilo Croce, Luca Iocchi, Roberto Basili, Daniele Nardi (2014): HuRIC: a Human Robot Interaction Corpus. In: Chair), Nicoletta Calzolari (Conference; Choukri, Khalid; Declerck, Thierry; Loftsson, Hrafn; Maegaard, Bente; Mariani, Joseph; Moreno, Asuncion; Odijk, Jan; Piperidis, Stelios (Ed.): Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), European Language Resources Association (ELRA), Reykjavik, Iceland, 2014, ISBN: 978-2-9517408-8-4.

Emanuele Bastianelli, Giuseppe Castellucci, Danilo Croce, Roberto Basili, Daniele Nardi (2014): Effective and Robust Natural Language Understanding for Human Robot Interaction. In: Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014), pp. 57 - 62, Prague, Czech Republic, 2014.

Bastianelli, Emanuele, Castellucci, Giuseppe, Croce, Danilo, Basili, Roberto (2013): Textual Inference and Meaning Representation in Human Robot Interaction. In: Joint Symposium on Semantic Processing: Textual Inference and Structures in Corpora, To Appear, Trento, Italy, 2013.

Roberto Basili, Emanuele Bastianelli, Giuseppe Castellucci, Daniele Nardi, Vittorio Perera (2013): Kernel-based Discriminative Re-ranking for Spoken Command Understanding in HRI. In: Baldoni, Matteo; Baroglio, Cristina; Boella, Guido; Micalizio, Roberto (Ed.): XIII Conference of the Italian Association for Artificial Intelligence, Springer, Torino, Italy, 2013, ISBN: 978-3-319-03523-9.