Abstract
Speech-based robot instruction is a promising field in private households and in small and medium-sized enterprises, because it facilitates a comfortable way of communicating with robot systems, even while the users’ hands are occupied. An essential problem in transforming the speech-based instructions into complex robot motions is the validation of the resulting motions regarding feasibility. This emerges from the fact that the user may not be fully aware of the capabilities of the robot including its gripper in a given working environment. We present an approach that tackles this problem by utilizing dynamic affordances for the disambiguation and elemental validation of speech-based instructions.
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Abelha, P., et al.: A model-based approach to finding substitute tools in 3d vision data. In: International Conference on Robotics and Automation (ICRA) (2016)
Carvalho, J.T., Nolfi, S.: Behavioural plasticity in evolving robots. Theory in Biosciences 135(4) (2016)
Chen, H., et al.: Enabling robots to understand incomplete natural language instructions using commonsense reasoning. arXiv:1904.12907 (2019)
Deits, R., et al.: Clarifying commands with information-theoretic human-robot dialog. Journal of Human-Robot Interaction 2(2) (2013)
Detry, R., et al.: Learning grasp affordance densities. Paladyn, Journal of Behavioral Robotics 2(1) (2011)
Gibson, J.J.: The theory of affordances. Hilldale, USA 1(2) (1977)
Gorniak, P., Roy, D.: Situated language understanding as filtering perceived affordances. Cognitive science 31(2) (2007)
Heikkilä, S.S., Halme, A., Schiele, A.: Affordance-based indirect task communication for astronaut-robot cooperation. Journal of Field Robotics 29(4) (2012)
Hemachandra, S., Walter, M.R., Teller, S.J.: Information theoretic question asking to improve spatial semantic representations. In: AAAI Fall Symposia (2014)
Horton, T.E., Chakraborty, A., Amant, R.S.: Affordances for robots: a brief survey. AVANT. Pismo Awangardy Filozo czno-Naukowej 2 (2012)
Jamone, L., et al.: Affordances in psychology, neuroscience, and robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems 10(1) (2016)
Kostavelis, I., et al.: Collision risk assessment for autonomous robots by offline traversability learning. Robotics and Autonomous Systems (2012)
Kroemer, O., Niekum, S., Konidaris, G.: A review of robot learning for manipulation: Challenges, representations, and algorithms. arXiv:1907.03146 (2019)
Lemaignan, S., et al.: Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence (2017)
Liu, C., Walker, J., Chai, J.Y.: Ambiguities in spatial language understanding in situated human robot dialogue. In: 2010 AAAI Fall Symposium Series (2010)
Marge, M., Rudnicky, A.: Miscommunication recovery in physically situated dialogue. In: Meeting of the Special Interest Group on Discourse and Dialogue (2015)
Marge, M., Rudnicky, A.I.: Miscommunication detection and recovery in situated human–robot dialogue. Transactions on Interactive Intelligent Systems (2019)
Márquez, L., Carreras, X., Litkowski, K.C., Stevenson, S.: Semantic role labeling: an introduction to the special issue (2008)
Min, H., Luo, R., Zhu, J., Bi, S., et al.: Affordance research in developmental robotics: A survey. Transactions on Cognitive and Developmental Systems (2016)
Min, H., et al.: Affordance learning and inference based on vision-speech association in human-robot interactions. International Conference on Robotics and Biomimetics (ROBIO) (2017)
Moratz, R., Tenbrink, T.: Affordance-based human-robot interaction. In: Towards Affordance-Based Robot Control. Springer (2008)
Song, H.O., Fritz, M., Goehring, D., Darrell, T.: Learning to detect visual grasp affordance. IEEE Transactions on Automation Science and Engineering 13(2) (2015)
Tellexll, S., Thakerll, P., Deitsl, R., Simeonovl, D., Kollar, T., Royl, N.: Toward information theoretic human-robot dialog. Robotics (2013)
Ugur, E., Oztop, E., Sahin, E.: Goal emulation and planning in perceptual space using learned affordances. Robotics and Autonomous Systems 59(7-8) (2011)
Williams, T., Yazdani, F., Suresh, P., Scheutz, M., Beetz, M.: Dempster-shafer theoretic resolution of referential ambiguity. Autonomous Robots 43(2) (2019)
Wölfel, K., Henrich, D.: Grounding verbs for tool-dependent, sensor-based robot tasks. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE (2018)
Zech, P., et al.: Computational models of affordance in robotics: a taxonomy and systematic classification. Adaptive Behavior (2017)
Zhu, Y., Zhao, Y., Chun Zhu, S.: Understanding tools: Task-oriented object modeling, learning and recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
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Wölfel, K., Henrich, D. (2020). Affordance Based Disambiguation and Validation in Human-Robot Dialogue. In: Schüppstuhl, T., Tracht, K., Henrich, D. (eds) Annals of Scientific Society for Assembly, Handling and Industrial Robotics. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61755-7_28
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DOI: https://doi.org/10.1007/978-3-662-61755-7_28
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