Abstract
The neural circuits that control grasping and perform related visual processing have been studied extensively in macaque monkeys. We are developing a computational model of this system, in order to better understand its function, and to explore applications to robotics. We recently modelled the neural representation of three-dimensional object shapes, and are currently extending the model to produce hand postures so that it can be tested on a robot. To train the extended model, we are developing a large database of object shapes and corresponding feasible grasps. Finally, further extensions are needed to account for the influence of higher-level goals on hand posture. This is essential because often the same object must be grasped in different ways for different purposes. The present paper focuses on a method of incorporating such higher-level goals. A proof-of-concept exhibits several important behaviours, such as choosing from multiple approaches to the same goal. Finally, we discuss a neural representation of objects that supports fast searching for analogous objects.
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Fagg, A.H., Arbib, M.A.: Modeling parietalpremotor interactions in primate control of grasping. Neural Networks 11(7–8), 1277–1303 (1998)
Borra, E., Gerbella, M., Rozzi, S., Luppino, G.: Anatomical evidence for the involvement of the macaque ventrolateral prefrontal area 12r in controlling goal-directed actions 31(34), 12351–12363 (2011)
Rezai, O., Kleinhans, A., Matallanas, E., Selby, B., Tripp, B.: Hierarchical object representations in the visual cortex and computer vision. Frontiers in Computational Neuroscience (in revision)
Cerri, G., Shimazu, H., Maier, M.A., Lemon, R.N.: Facilitation from ventral premotor cortex of primary motor cortex outputs to macaque hand muscles 90(2), 832–842 (2003)
Eliasmith, C., Anderson, C.: Neural engineering. MIT Press (2003)
Plate, T.A.: Holographic Reduced Representation: Distributed representation for cognitive structures. Center for the Study of Language and Inf. (2003)
Eliasmith, C., Stewart, T.C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., Rasmussen, D.: A large-scale model of the functioning brain. Science 338(6111), 1202–1205 (2012). PMID: 23197532
Berzish, M., Tripp, B.: A digital hardware design for real-time simulation of large neural-system models in physical settings. In: CNS (2014)
Eliasmith, C.: How to build a brain: A neural architecture for biological cognition. Oxford University Press (2013)
Gold, J.I., Shadlen, M.N.: The neural basis of decision making 30, 535–574 (2007)
Cisek, P.: Making decisions through a distributed consensus. Current Opinion in Neurobiology 22(6), 927–936 (2012)
Montesano, L., Lopes, M., Bernardino, A., Santos-Victor, J.: Learning object affordances: From sensory-motor coordination to imitation. IEEE Transactions on Robotics 24(1), 15–26 (2008)
Stoytchev, A.: Learning the affordances of tools using a behavior-grounded approach. In: Rome, E., Hertzberg, J., Dorffner, G. (eds.) Towards Affordance-Based Robot Control. LNCS (LNAI), vol. 4760, pp. 140–158. Springer, Heidelberg (2008)
Sahin, E., Cakmak, M., Dogar, M.R., Ugur, E., Ucoluk, G.: To afford or not to afford: a new formalization of affordances toward affordance-based robot control. In: Adaptive Behavior (2007)
Sun, J., Garibaldi, J.: A novel memetic algorithm for constrained optimization. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Krüger, N., Piater, J., Geib, C., Petrick, R., Steedman, M., Wrgtter, F., Ude, A., Asfour, T., Kraft, D., Omren, D., Agostini, A., Dillmann, R.: Objectaction complexes: grounded abstractions of sensorymotor processes. In: Robotics and Autonomous Systems (2011)
Detry, R., Baseski, E., Krüger, N., Popovic, M., Touati, Y., Kroemer, O., Peters, J., Piater, J.: Learning object-specific grasp affordance densities. In: IEEE International Conference on Development and Learning, pp. 1–7 (2009)
Kjellström, H., Romero, J., Kragic, D.: Visual object-action recognition: Inferring object affordances from human demonstration. Computer Vision and Image Understanding 115(1), 81–90 (2011)
Thill, S., Caligiore, D., Borghi, A.M., Ziemke, T., Baldassarre, G.: Theories and computational models of affordance and mirror systems: an integrative review. Neuroscience & Bio Behavioral Reviews 37(3), 491–521 (2013)
Caligiore, D., Borghi, A.M., Parisi, D., Baldassarre, G.: Tropicals: a computational embodied neuroscience model of compatibility effects. Psychological Review 117(4), 1188 (2010)
Oztop, E., Imamizu, H., Cheng, G., Kawato, M.: A computational model of anterior intraparietal (aip) neurons. Neurocomputing 69(10–12), 1354–1361 (2006)
Oztop, E., Kawato, M., Arbib, M.A.: Mirror neurons and imitation: A computationally guided review. Neural Networks 19, 254–271 (2006)
Oztop, E., Imamizu, H., Cheng, G., Kawato, M.: Neurocomputing 69(10–12), 1354–1361 (June 2006)
Thill, S., Svensson, H., Ziemke, T.: Modeling the development of goal-specificity in mirror neurons. Cognitive Computation 3(4), 525–538 (2011)
Murata, A., Gallese, V., Luppino, G., Kaseda, M., Sakata, H.: Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area aip 83(5), 2580–2601 (2000). PMID: 10805659
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Kleinhans, A., Thill, S., Rosman, B., Detry, R., Tripp, B. (2015). Modelling Primate Control of Grasping for Robotics Applications. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8926. Springer, Cham. https://doi.org/10.1007/978-3-319-16181-5_33
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DOI: https://doi.org/10.1007/978-3-319-16181-5_33
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