Abstract
In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users and robots. With the existing HRI pipelines, users can teach the robots some concepts to increase their knowledge base. Unfortunately, the data coming from the users are usually not enough dense for building a consistent representation. Furthermore, the existing approaches are not able to incrementally build up their knowledge base, which is very important when robots have to deal with dynamic contexts. To this end, we propose an architecture to gather data from users and environments in long-runs of continual learning. We adopt Knowledge Graph Embedding techniques to generalize the acquired information with the goal of incrementally extending the robot’s inner representation of the environment. We evaluate the performance of the overall continual learning architecture by measuring the capabilities of the robot of learning entities and relations coming from unknown contexts through a series of incremental learning sessions.
E. Bartoli and F. Argenziano—These two authors contributed equally.
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
Aljundi, R., Kelchtermans, K., Tuytelaars, T.: Task-free continual learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11254–11263 (2019)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
Gemignani, G., Capobianco, R., Bastianelli, E., Bloisi, D.D., Iocchi, L., Nardi, D.: Living with robots: interactive environmental knowledge acquisition. Robot. Auton. Syst. 78, 1–16 (2016)
Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2021)
Kolve, E., et al.: AI2-THOR: an interactive 3D environment for visual AI. arXiv preprint arXiv:1712.05474 (2017)
Laird, J.E., et al.: Interactive task learning. IEEE Intell. Syst. 32(4), 6–21 (2017)
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935–2947 (2018). https://doi.org/10.1109/TPAMI.2017.2773081
Lindblom, J., Andreasson, R.: Current challenges for UX evaluation of human-robot interaction. In: Schlick, C., Trzcieliński, S. (eds.) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future, pp. 267–277. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41697-7_24
Nickel, M., Tresp, V., Kriegel, H.P.: A three-way model for collective learning on multi-relational data. In: ICML (2011)
Perdomo, J., Zrnic, T., Mendler-Dünner, C., Hardt, M.: Performative prediction. In: International Conference on Machine Learning, pp. 7599–7609. PMLR (2020)
Pronobis, A.: Semantic mapping with mobile robots. Ph.D. thesis, KTH Royal Institute of Technology (2011)
Pronobis, A., Jensfelt, P.: Large-scale semantic mapping and reasoning with heterogeneous modalities. In: 2012 IEEE International Conference on Robotics and Automation, pp. 3515–3522. IEEE (2012)
Randelli, G., Bonanni, T.M., Iocchi, L., Nardi, D.: Knowledge acquisition through human-robot multimodal interaction. Intel. Serv. Robot. 6(1), 19–31 (2013)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bartoli, E., Argenziano, F., Suriani, V., Nardi, D. (2023). Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions Using Knowledge Graph Embedding. In: Dovier, A., Montanari, A., Orlandini, A. (eds) AIxIA 2022 – Advances in Artificial Intelligence. AIxIA 2022. Lecture Notes in Computer Science(), vol 13796. Springer, Cham. https://doi.org/10.1007/978-3-031-27181-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-27181-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27180-9
Online ISBN: 978-3-031-27181-6
eBook Packages: Computer ScienceComputer Science (R0)