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.
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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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