Abstract
Dynamic tactile exploration enables humans to seamlessly estimate the shape of objects and distinguish them from one another in the complete absence of visual information. Such a blind tactile exploration allows integrating information of the hand pose and contacts on the skin to form a coherent representation of the object shape. A principled way to understand the underlying neural computations of human haptic perception is through normative modelling. We propose a Bayesian perceptual model for recursive integration of noisy proprioceptive hand pose with noisy skin–object contacts. The model simultaneously forms an optimal estimate of the true hand pose and a representation of the explored shape in an object–centred coordinate system. A classification algorithm can, thus, be applied in order to distinguish among different objects solely based on the similarity of their representations. This enables the comparison, in real–time, of the shape of an object identified by human subjects with the shape of the same object predicted by our model using motion capture data. Therefore, our work provides a framework for a principled study of human haptic exploration of complex objects.
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References
Amedi, A., Malach, R., Hendler, T., Peled, S., Zohary, E.: Visuo-haptic object-related activation in the ventral visual pathway. Nature Neurosci. 4(3), 324–330 (2001)
Behbahani, F.M., Taunton, R., Thomik, A.A., Faisal, A.A.: Haptic slam for context-aware robotic hand prosthetics-simultaneous inference of hand pose and object shape using particle filters. In: 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 719–722. IEEE (2015)
Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001)
Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)
Faisal, A.A., Selen, L.P., Wolpert, D.M.: Noise in the nervous system. Nat. Rev. Neurosci. 9(4), 292–303 (2008)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007)
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., et al.: Fastslam: a factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)
Newell, F.N., Ernst, M.O., Tjan, B.S., Bülthoff, H.H.: Viewpoint dependence in visual and haptic object recognition. Psychol. Sci. 12(1), 37–42 (2001)
Santello, M., Flanders, M., Soechting, J.F.: Postural hand synergies for tool use. J. Neurosci. 18(23), 10105–10115 (1998)
Smith, A., Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer Science & Business Media, Berlin (2013)
Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Int. J. Robot. Res. 5(4), 56–68 (1986)
Van Beers, R., Sittig, A.C., van der Gon, J.J.D.: The precision of proprioceptive position sense. Exp. Brain Res. 122(4), 367–377 (1998)
Wolpert, D.M., Ghahramani, Z., Jordan, M.I.: An internal model for sensorimotor integration. Science 269(5232), 1880 (1995)
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Behbahani, F.M.P., Singla–Buxarrais, G., Faisal, A.A. (2016). Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics. In: Bello, F., Kajimoto, H., Visell, Y. (eds) Haptics: Perception, Devices, Control, and Applications. EuroHaptics 2016. Lecture Notes in Computer Science(), vol 9774. Springer, Cham. https://doi.org/10.1007/978-3-319-42321-0_14
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DOI: https://doi.org/10.1007/978-3-319-42321-0_14
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