Analyzing the Relevance of Features for a Social Navigation Task
Robot navigation in human environments is an active research area that poses serious challenges in both robot perception and actuation. Among them, social navigation and human-awareness have gained lot of attention in the last years due to its important role in human safety and robot acceptance. Several approaches have been proposed; learning by demonstrations stands as one of the most used approaches for estimating the insights of human social interactions. However, typically the features used to model the person-robot interaction are assumed to be given. It is very usual to consider general features like robot velocity, acceleration or distance to the persons, but there are not studies on the criteria used for such features selection.
In this paper, we employ a supervised learning approach to analyze the most important features that might take part into the human-robot interaction during a robot social navigation task. To this end, different subsets of features are employed with an AdaBoost classifier and its classification accuracy is compared with that of humans in a social navigation experimental setup. The analysis shows how it is very important not only to consider the robot-person relative poses and velocities, but also to recognize the particular social situation.
KeywordsHuman-robot interaction Supervised learning Social robot
Unable to display preview. Download preview PDF.
- 1.Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the twenty-first international conference on Machine learning, ICML 2004, p. 1. ACM, New York (2004). http://doi.acm.org/10.1145/1015330.1015430
- 3.Arras, K.O., Mozos, O.M., Burgard, W.: Using boosted features for the detection of people in 2d range data. In: Proc. International Conference on Robotics and Automation, ICRA (2008)Google Scholar
- 4.Desai, M., Tsui, K., Yanco, H., Uhlik, C.: Essential features of telepresence robots. In: 2011 IEEE Conference on Technologies for Practical Robot Applications (TePRA), pp. 15–20, April 2011Google Scholar
- 5.Ferrer, G., Garrell, A., Sanfeliu, A.: Robot companion: a social-force based approach with human awareness-navigation in crowded environments. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1688–1694, Nov 2013Google Scholar
- 6.Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997). http://www.sciencedirect.com/science/article/pii/S002200009791504X CrossRefMathSciNetMATHGoogle Scholar
- 7.Henry, P., Vollmer, C., Ferris, B., Fox, D.: Learning to navigate through crowded environments. In: ICRA 2010, pp. 981–986 (2010)Google Scholar
- 8.Knox, W., Spaulding, S., Breazeal, C.: Learning social interaction from the wizard: a proposal. In: Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)Google Scholar
- 9.Luber, M., Spinello, L., Silva, J., Arras, K.: Socially-aware robot navigation: a learning approach. In: IROS, pp. 797–803. IEEE (2012)Google Scholar
- 10.Perez-Higueras, N., Ramon-Vigo, R., Caballero, F., Merino, L.: Robot local navigation with learned social cost functions. In: 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 02, pp. 618–625, Sept 2014Google Scholar
- 11.Stein, P., Spalanzani, A., Santos, V., Laugier, C.: On leader following and classification. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 3135–3140. IEEE (2014)Google Scholar
- 12.Trautman, P., Krause, A.: Unfreezing the robot: navigation in dense, interacting crowds. In: IROS, pp. 797–803. IEEE (2010)Google Scholar
- 13.Tsui, K., Desai, M., Yanco, H., Uhlik, C.: Exploring use cases for telepresence robots. In: 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 11–18, March 2011Google Scholar
- 14.Vasquez, D., Okal, B., Arras, K.O.: Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2014). Chicago (2014)Google Scholar
- 15.Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004). http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb CrossRefGoogle Scholar