Abstract
By enabling users to teach behaviors to robots, social robots become more adaptable, and therefore more acceptable. We improved an application for teaching behaviors to support conditions closer to the real-world: it supports spoken instructions, and remain compatible the robot’s other purposes. We introduce a novel architecture to enable 5 distinct algorithms to compete with each other, and a novel teaching algorithm that remain robust with these constraints: using linguistics and semantics, it can recognize when the dialogue context is adequate. We carry out an adaptation of a previous experiment, so that to produce comparable results, demonstrate that all participants managed to teach new behaviors, and partially verify our hypotheses about how users naturally break down the teaching instructions.
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Notes
- 1.
- 2.
NAOqi is the software running by default on Pepper and NAO robots.
- 3.
libQi provides NAOqi’s RPC mechanisms: https://github.com/aldebaran/libqi.
References
Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T.: Power to the people: the role of humans in interactive machine learning. AI Mag. 35(4), 105–120 (2014)
Beetz, M., Mösenlechner, L., Tenorth, M.: CRAM - a cognitive robot abstract machine for everyday manipulation in human environments. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1012–1017. IEEE, October 2010. https://doi.org/10.1109/IROS.2010.5650146
Chernova, S., Thomaz, A.L.: Robot Learning from Human Teachers. Morgan & Claypool, San Rafael (2014)
Dominey, P.F., Paléologue, V., Pandey, A.K., Ventre-Dominey, J.: Improving quality of life with a narrative companion. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal, September 2017
Dominey, P.F., et al.: Cooperative human robot interaction with the Nao humanoid: technical description paper for the “radical dudes”. RoboCup@ Home Technical Description Papers: Germany-Singapore (2010)
Fillmore, C.J.: Frames and the semantics of understanding. Quaderni di Semantica 6(2), 222–254 (1985)
Fischer, T., et al.: iCub-HRI: a software framework for complex human-robot interaction scenarios on the iCub humanoid robot. Front. Robot. AI 5 (2018). https://doi.org/10.3389/frobt.2018.00022
Gemignani, G., Bastianelli, E., Nardi, D.: Teaching robots parametrized executable plans through spoken interaction. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 851–859. International Foundation for Autonomous Agents and Multiagent Systems (2015)
Laird, J.E., et al.: Interactive task learning. IEEE Intell. Syst. 32(4), 6–21 (2017). https://doi.org/10.1109/MIS.2017.3121552
Lauria, S., Bugmann, G., Kyriacou, T., Bos, J., Klein, E.: Personal robot training via natural-language instructions. IEEE Intell. Syst. 16, 38–45 (2001)
Liu, C., et al.: Jointly learning grounded task structures from language instruction and visual demonstration, pp. 1482–1492. Association for Computational Linguistics (2016). https://doi.org/10.18653/v1/D16-1155
Nyga, D.: Interpretation of natural-language robot instructions. Ph.D. thesis, University of Bremen - Institute of Artificial Intelligence, Bremen, April 2017
Paléologue, V., Martin, J., Pandey, A.K., Coninx, A., Chetouani, M.: Semantic-based interaction for teaching robot behavior compositions. In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal, September 2017
Rybski, P.E., Yoon, K., Stolarz, J., Veloso, M.M.: Interactive robot tasktraining through dialog and demonstration. In: Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 49–56. ACM (2007). https://doi.org/10.1145/1228716.1228724
Schermerhorn, P., Kramer, J., Brick, T., Anderson, D., Dingler, A., Scheutz, M.: DIARC: A Testbed for Natural Human-Robot Interaction, p. 8 (2006)
Scheutz, M., Krause, E., Oosterveld, B.: Spoken Instruction-Based One-Shot Object and Action Learning in a Cognitive Robotic Architecture, p. 9 (2018)
Sorce, M., Pointeau, G., Petit, M., Mealier, A.L., Gibert, G., Dominey, P.F.: Proof of concept for a user-centered system for sharing cooperative plan knowledge over extended periods and crew changes in space-flight operations. In: The 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Kobe, Japan, pp. 776–783. IEEE, August 2015. https://doi.org/10.1109/ROMAN.2015.7333565
Tenorth, M., Nyga, D., Beetz, M.: Understanding and executing instructions for everyday manipulation tasks from the world wide web. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 1486–1491. IEEE (2010)
Trafton, G., Hiatt, L., Harrison, A., Tamborello, F., Khemlani, S., Schultz, A.: ACT-R/E: an embodied cognitive architecture for human-robot interaction. J. Hum.-Robot Interact. 2(1), 30–55 (2013)
Vollmer, A.L., Wrede, B., Rohlfing, K.J., Oudeyer, P.Y.: Pragmatic frames for teaching and learning in human-robot interaction: review and challenges. Front. Neurorobotics 10, 1–20 (2016). https://doi.org/10.3389/fnbot.2016.00010
Weitzenfeld, A., Dominey, P.F.: Cognitive robotics: command, interrogation and teaching in robot coaching. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 379–386. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74024-7_36
Acknowledgments
This work was partially supported by the European Union’s Horizon 2020 project Culture Aware Robots and Environmental Sensor Systems for Elderly Support (http://www.caressesrobot.org) under grant 737858; by the Ministry of Internal Affairs and Communication of Japan; and by the European Union’s Horizon 2020 project MultiModal Mall Entertainment Robot (http://www.mummer-project.eu) research and innovation program under grant 688147.
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Paléologue, V., Martin, J., Pandey, A.K., Chetouani, M. (2018). Semantic-Based Interaction for Teaching Robot Behavior Compositions Using Spoken Language. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_41
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