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Article published In:
Interaction Studies
Vol. 15:1 (2014) ► pp.1–37

Verb concepts from affordances

In this paper, we investigate how the interactions of a robot with its environment can be used to create concepts that are typically represented by verbs in language. Towards this end, we utilize the notion of affordances to argue that verbs typically refer to the generation of a specific type of effect rather than a specific type of action. Then, we show how a robot can form these concepts through interactions with the environment and how humans can use these concepts to ease their communication with the robots. We demonstrate that iCub, a humanoid robot, can use the concepts, which it has developed, to to understand what a human performs, perform multi-step planning for reaching a goal state as well as to specify a goal to the robot using symbolic descriptions.

Published online: 10 June 2014
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https://doi.org/10.1075/is.15.1.01kal
References
Akgun, B., Dag, N., Bilal, T., Atil, I., & Sahin, E. (
2009). Unsupervised learning of affordance relations on a humanoid robot. 24th International Symposium on Computer and Information Sciences (ISCIS), 254–259.Google Scholar
Akgun, B., Tunaoglu, D., & Sahin, E. (
2010). Action recognition through an action generation mechanism. International Conference on Epigenetic Robotics.Google Scholar
Alissandrakis, A., Nehaniv, C., & Dautenhahn, K. (
2003). Syn-chrony and perception in robotic imitation across embodiments. In Computational intelligence in robotics and automation, 2003. proceedings. 2003. ieee international symposium on (Vol. 21, pp. 923–930).
Ashby, F.G., & Maddox, W.T. (
1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372–400. DOI logoGoogle Scholar
Barsalou, L. (
1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 577–660.. DOI logoGoogle Scholar
Bergquist, T., Schenck, C., Ohiri, U., Sinapov, J., Griffith, S., & Stoytchev, A. (
2009). Interactive object recognition using pro-prioceptive feedback. IROS Workshop: Semantic Perception for Mobile Manipulation .
Borghi, A.M. (
2007). Object concepts and embodiment: Why sensorimotor and cognitive processes cannot be separated. La nuova Critica, 15(4), 447–472.Google Scholar
2012). Action language comprehension, affordances and goals. InY. Coello & A. Bartolo(Eds.), Language and action in cognitive neuroscience. contemporary topics in cognitive neuroscience series (pp. 125–143). Psychology Press.Google Scholar
Borghi, A.M., & Riggio, L. (
2009). Sentence comprehension and simulation of object temporary, canonical and stable affordances. Brain Research, 12531, 117–128. DOI logoGoogle Scholar
Bruner, J., Goodnow, J., & Austin, G. (
1986). A study of thinking. Transaction Publishers. Google Scholar
Cangelosi, A. (
2001). Evolution of communication and language using signals, symbols, and words. IEEE Transactions on Evolutionary Computation, 5(2), 93–101. DOI logoGoogle Scholar
2010). Grounding language in action and perception: From cognitive agents to humanoid robots. Physics of Life Reviews, 7(2), 139–151. DOI logoGoogle Scholar
Cangelosi, A., & Harnad, S. (
2001). The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories. Evolution of Communication, 4(1), 117–142. DOI logoGoogle Scholar
Cangelosi, A., Hourdakis, E., & Tikhanoff, V. (
2006). Language acquisition and symbol grounding transfer with neural networks and cognitive robots. International Joint Conference on Neural Networks (IJCNN), 1576–1582.Google Scholar
Cangelosi, A., Metta, G., Sagerer, G., Nolfi, S., Nehaniv, C., Fischer, K., & Zeschel, A. (
2010). Integration of action and language knowledge: A roadmap for developmental robotics. IEEE Transactions on Autonomous Mental Development, 2(3), 167–195. DOI logoGoogle Scholar
Cangelosi, A., & Parisi, D. (
2004). The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging. Brain and Language, 89(2), 401–408. DOI logoGoogle Scholar
Cangelosi, A., & Riga, T. (
2006). An embodied model for sensorimotor grounding and grounding transfer: Experiments with epigenetic robots. Cognitive Science, 30(4), 673–689. DOI logoGoogle Scholar
Christiansen, M., & Kirby, S. (
2003). Language evolution: Consensus and controversies. Trends in Cognitive Sciences, 7(7), 300–307. DOI logoGoogle Scholar
Cohen, P., Morrison, C., & Cannon, E. (
2005). Maps for verbs: The relation between interaction dynamics and verb use. Proceedings of the 9th International Conference on Artificial Intelligence (ijcai).Google Scholar
Elsner, B. (
2007). Infants’ imitation of goaldirected actions: The role of movements and action effects. Acta Psychologica, 124(1), 44–59. DOI logoGoogle Scholar
Fischer, M., & Zwaan, R. (
2008). Embodied language: A review of the role of the motor system in language comprehension. The Quarterly Journal of Experimental Psychology, 61(6), 825–850. DOI logoGoogle Scholar
Gabora, L., Rosch, E., & Aerts, D. (
2008). Toward an ecological theory of concepts. Ecological Psychology, 20(1), 84–116. DOI logoGoogle Scholar
Gallese, V., & Lakoff, G. (
2005). The brain’s concepts: The role of the sensory-motor system in conceptual knowledge. Cognitive Neuropsychology, 22(3), 455–479. DOI logoGoogle Scholar
Gärdenfors, P. (
2004). Conceptual spaces: The geometry of thought. The MIT Press.Google Scholar
Gibson, J.J. (
1986). The ecologial approach to visual perception. Lawrence Erlbaum Associates.Google Scholar
Glenberg, A., & Kaschak, M. (
2002). Grounding language in action. Psychonomic Bulletin and Review, 9(3), 558. DOI logoGoogle Scholar
Glenberg, A., & Robertson, D. (
2000). Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning. Journal of Memory and Language, 43(3), 379–401. DOI logoGoogle Scholar
Glenberg, A., Sato, M., Cattaneo, L., Riggio, L., Palumbo, D., & Buccino, G. (
2008). Processing abstract language modulates motor system activity. The Quarterly Journal of Experimental Psychology, 61(6), 905–919. DOI logoGoogle Scholar
Hamilton, A., Grafton, S., &
Hamilton, A. (2007). The motor hierarchy: from kinematics to goals and intentions. InP. Haggard, Y. Rossetti, & M. Kawato(Eds.), Sensorimotor foundations of higher cognition, attention and performance (pp. 381–408). Oxford University Press.
Harnad, S. (
1990). The symbol grounding problem. Physica, D(42), 335–346.Google Scholar
Hashimoto, T., & Masumi, A. (
2007). Learning and transition of symbols: Towards a dynamical model of a symbolic individual. InC.N.C. Lyon & A. Cangelos(Eds.), Emergence of communication and language (pp. 223–236). Springer. DOI logoGoogle Scholar
Hommel, B., Musseler, J., Aschersleben, G., & Prinz, W. (
2001). The theory of event coding (tec): A framework for perception and action planning. Behavioral and Brain Sciences, 24(05), 849–878. DOI logoGoogle Scholar
Jebara, T. (
2004). Machine learning: Discriminative and generative (Vol. 7551). Springer.. DOI logoGoogle Scholar
Johansen, M., & Kruschke, J. (
2005). Category representation for classification and feature inference. Learning Memory, 31(6), 1433–1458. DOI logoGoogle Scholar
Kozima, H., Nakagawa, C., & Yano, H. (
2002). Emergence of imitation mediated by objects. Lund University Cognitive Studies, 59–61.Google Scholar
Krunic, V., Salvi, G., Bernardino, A., Montesano, L., & Santos-Victor, J. (
2009). Affordance based word-to-meaning association. IEEE Int. Conference on Robotics and Automation (ICRA), 4138–4143.
Kruschke, J. (
2005). Category learning. In:K. Lamberts, & R.L. Goldstone(Eds.), The handbook of cognition, 183–201.. DOI logoGoogle Scholar
Leopold, D., O’Toole, A., Vetter, T., & Blanz, V. (
2001). Prototypereferenced shape encoding revealed by high-level aftereffects. Nature Neuroscience, 4(1), 89–94. DOI logoGoogle Scholar
Lyon, C., Nehaniv, C., & Cangelosi, A. (
2007). Emergence of communication and language. Springer-VerlagNew York Inc. DOI logoGoogle Scholar
Mahalanobis, P. (
1936). On the generalized distance in statistics. In Proceedings of the National Institute of Sciences of India (Vol. 21, pp. 49–55).
Marocco, D., Cangelosi, A., Fischer, K., & Belpaeme, T. (
2010). Grounding action words in the sensorimotor interaction with the world: Experiments with a simulated iCub humanoid robot. Frontiers in Neurorobotics, 4(7), 1–15.Google Scholar
Metta, G., & Fitzpatrick, P. (
2003). Better vision through manipulation. Adaptive Behavior, 11(2), 109–128. DOI logoGoogle Scholar
Metta, G., Sandini, G., Vernon, D., Natale, L., & Nori, F. (
2008). The iCub humanoid robot: An open platform for research in embodied cognition. In Proceedings ofthe 8th workshop on performance metrics for intelligent systems (pp. 50–56).
Minda, J., & Smith, J. (
2001). Prototypes in category learning: The effects of category size, category structure, and stimulus com-plexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(3), 775–799. DOI logoGoogle Scholar
Montesano, L., Lopes, M., Bernardino, A., & Santos-Victor, J. (
2008). Learning object affordances: From sensory-motor co-ordination to imitation. IEEE Transactions on Robotics, 24(1), 15–26. DOI logoGoogle Scholar
Montesano, L., Lopes, M., Melo, F., Bernardino, A., & Santos-Victor, J. (
2009). A computational model of object affordances. Advances in Cognitive Systems.Google Scholar
Nehaniv, C.L., Lyon, C., & Cangelosi, A. (
2007). Current work and open problems: A road-map for research into the emergence of communication and language. InC.L.N.C. Lyon, & A. Cangelosi(Eds.), Emergence of communication and language (pp. 1–27). Springer. DOI logoGoogle Scholar
Nosofsky, R., Kruschke, J., & McKinley, S. (
1992). Combining exemplar-based category representations and connectionist learning rules. Learning, Memory, 18(2), 211–233. DOI logoGoogle Scholar
Nosofsky, R., & Zaki, S. (
2002). Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Learning Memory, 28(5), 924–940. DOI logoGoogle Scholar
Parthemore, J., & Morse, A. (
2010). Representations reclaimed: Accounting for the co-emergence of concepts and experience. Pragmatics and Cognition, 18(2), 273–312. DOI logoGoogle Scholar
Qin, A., & Suganthan, P. (
2004). Robust growing neural gas algorithm with application in cluster analysis. Neural Networks, 17(8–9), 1135–1148. DOI logoGoogle Scholar
Rosch, E. (
1973). Natural categories. Cognitive Psychology, 4(3), 328–350. DOI logoGoogle Scholar
Rosseel, Y. (
2002). Mixture models of categorization. Journal of Mathematical Psychology, 46(2), 178–210. DOI logoGoogle Scholar
Rouder, J., & Ratcliff, R. (
2006). Comparing exemplar-and rule-based theories of categorization. Current Directions in Psychological Science, 15(1), 9–13. DOI logoGoogle Scholar
Rudolph, M., Muhlig, M., Gienger, M., & Bohme, H.-J. (
2010). Learning the consequences of actions: Representing effects as feature changes. Int. Symposium on Learning and Adaptive Behavior in Robotic Systems .
Rusu, R.B., & Cousins, S. (
2011). 3d is here: Point cloud library (pcl). Library, 26(2), 1–4.Google Scholar
Sahin, E., Cakmak, M., Dogar, M., Ugur, E., & Ucoluk, G. (
2007). To afford or not to afford: A new formalization of affordances toward affordance-based robot control. Adaptive Behavior, 15(4), 447–472. DOI logoGoogle Scholar
Steels, L. (
2003). Evolving grounded communication for robots. Trends in Cognitive Science, 7(7), 308–312. DOI logoGoogle Scholar
2007). The recruitment theory of language origins. InC.L.N.C. Lyon, & A. Cangelosi(Eds.), Emergence of communication and language (pp. 129–150). Springer. DOI logoGoogle Scholar
Ug̃ur, E., Sahin, E., & Oztop, E. (
2009). Affordance learning from range data for multi-step planning. 9th International Conference on Epigenetic Robotics (Epirob) , 1461, 177–184.
Uğur, E., & Şahin, E. (
2010). Traversability: A case study for learning and perceiving affordances in robots. Adaptive Behavior, 18(3–4), 258–284. DOI logoGoogle Scholar
Umilta, M., Intskirveli, I., Grammont, F., Rochat, M., Caruana, F., Jezzini, A., & Rizzolatti, G. (
2008). When pliers become fingers in the monkey motor system. Proceedings of the National Academy of Sciences, 105(6), 2209. DOI logoGoogle Scholar
Umilta, M., Kohler, E., Gallese, V., Fogassi, L., Fadiga, L., Keysers, C., & Rizzolatti, G. (
2001). I know what you are doing: A neurophysiological study. Neuron, 31(1), 155–165. DOI logoGoogle Scholar
Verguts, T., Ameel, E., & Storms, G. (
2004). Measures of similarity in models of categorization. Memory and Cognition, 32(3), 379. DOI logoGoogle Scholar
Want, S.C., & Harris, P.L. (
2002). How do children ape? Applying concepts from the study of non-human primates to the developmental study of imitation in children. Developmental Science, 5(1), 1–13. DOI logoGoogle Scholar
Zwaan, R., & Taylor, L. (
2006). Seeing, acting, understanding: Motor resonance in language comprehension. Journal of Experimental Psychology-General, 135(1), 1–11. DOI logoGoogle Scholar
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