Abstract
General-purpose robots operating in unstructured environments have the potential to benefit by leveraging abstract, commonsense knowledge for task execution. In this paper, we present an approach for automatically generating a compact semantic knowledge base, relevant to a robot’s particular operating environment, given only a small number of object labels obtained from object recognition or a robot’s task description. In order to cope with noise and non-deterministic data across our data sources, we formulate our representation as a statistical relational model represented as a Baysian Logic Network. We validate our approach in both abstract and real-world domains, demonstrating the robot’s ability to perform inference about object categories, locations and properties given a small amount of local information. Additionally, we present an approach for interactively validating the mined information with the help of a co-located user.
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
Notes
- 1.
The gold standard was generated by hand based on commonsense information (e.g., UsedFor(Knife,Cut) is true), and then validated by comparing to crowd-generated labels from five crowd workers (0.8 agreement threshold). A comparison between hand-labeled and crowd-labeled data resulted in accuracy values within 1% for all tested instances.
- 2.
This approach could also be combined with crowdsourcing, although we relied on a co-present expert for all experiments described here.
- 3.
We ignore properties with value < 0.07.
References
Boteanu, A., Chernova, S.: Solving and explaining analogy questions using semantic networks. In: AAAI Conference on Artificial Intelligence, pp. 1–8 (2015)
Buschka, P., Saffiotti, A.: Some notes on the use of hybrid maps for mobile robots. In: International Conference on Intelligent Autonomous Systems, pp. 547–556 (2004)
Chen, J., Liu, J.: Combining conceptnet andwordnet forword sense disambiguation. In: International Joint Conference on Natural Language Processing, pp. 686–694 (2011)
Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Fung, R.M., Chang, K-C.: Weighing and integrating evidence for stochastic simulation in Bayesian networks. In: Conference on Uncertainty in Artificial Intelligence, pp. 209–220 (1990)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: International Joint Conference on Artificial Intelligence, pp. 1606–1611 (2007)
Jain, D., Kirchlechner, B., Beetz, M.: Extending markov logic to model probability distributions in relational domains. In: KI 2007: Advances in Artificial Intelligence, pp. 129–143. Springer (2007)
Jain, D., Waldherr, S., Beetz, M.: Bayesian logic networks. Technical report, Technische Universität München, pp. 1–21 (2009)
Kucera, H., Francis, W.N.: Computational Analysis of Present-Day American English. Brown University Press, Providence (1967)
Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)
Lin, T-Y., et al. Microsoft COCO: common objects in context. In: CoRR arXiv:1405.0312 (2014)
Matuszek, C., et al.: An introduction to the syntax and content of Cyc. In: AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering, pp. 44–49 (2006)
Miller, George A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE (2016)
Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)
Saxena, A., et al.: Robobrain: large-scale knowledge engine for robots. arXiv:1412.0691 (2014)
Seco, N., et al.: An intrinsic information content metric for semantic similarity in wordnet (2004)
Speer, R., Havasi, C.: Representing general relational knowledge in conceptnet 5. In: International Conference on Language Resources and Evaluation. Istanbul (2012)
Stoica, E., Hearst, M.A.: Nearly-automated metadata hierarchy creation. In: North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 117–120 (2004)
Tenorth, M., Beetz, M.: KnowRob-knowledge processing for autonomous personal robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4261–4266. IEEE (2009)
Tsatsaronis, G., Varlamis, I., Vazirgiannis, M.: Word sense disambiguation with semantic networks". In: Sojka, P., et al. (eds.) Text, Speech, and Dialogue, pp. 219–226. Springer, Berlin (2008)
Waibel, Markus, et al.: Roboearth. IEEE Robot. Autom. Mag. 18(2), 69–82 (2011)
Xiao, J., et al.: SUN database: large-scale scene recognition from abbey to zoo. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3485–3492. IEEE (2010)
Zhu, Y., Fathi, A., Fei-Fei, L.: Reasoning about object affordances in a knowledge base representation. In: European Conference on Computer Vision, pp. 408–424. Springer (2014)
Acknowledgements
This work is supported in part by NSF IIS 1564080 and ONR N000141612835.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chernova, S. et al. (2020). Situated Bayesian Reasoning Framework for Robots Operating in Diverse Everyday Environments. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-28619-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28618-7
Online ISBN: 978-3-030-28619-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)