Abstract
Purposive and systematic movements are required for the exploration of tactile properties. Obtaining precise spatial details of the shape of an object with tactile data requires a dynamic edge following exploratory procedure. The contour following task relies on the perception of the angle and position of the sensor relative to the edge of the object. The perceived angle determines the direction of exploratory actions, and the position indicates the location relative to the edge for placing the sensor where the angle tends to be perceived more accurately. Differences in the consistency of the acquired tactile data during the execution of the task might induce inaccuracies in the predictions of the sensor model, and therefore impact on the enactment of active and exploratory movements. This work examines the influence of integrating information from robot proprioception to assess the accuracy of a Bayesian model and update its parameters to enhance the perception of angle and position of the sensor. The incorporation of proprioceptive information achieves an increased number of task completions relative to performing the task with a model trained with tactile data collected offline. Studies in biological touch suggest that tactile and proprioceptive information contribute synergistically to the perception of geometric properties and control of the sensory apparatus; this work proposes a method for the improvement of perception of the magnitudes required to actively follow the contour of an object under the presence of variability in the acquired tactile data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chorley, C., Melhuish, C., Pipe, T., Rossiter, J.: Development of a tactile sensor based on biologically inspired edge encoding. In: 2009 International Conference on Advanced Robotics, pp. 1–6 (2009). https://ieeexplore.ieee.org/document/5174720
Corniani, G., Saal, H.P.: Tactile innervation densities across the whole body. J. Neurophysiol. 124(4), 1229–1240 (2020). https://doi.org/10.1152/jn.00313.2020
Driess, D., Hennes, D., Toussaint, M.: Active multi-contact continuous tactile exploration with Gaussian process differential entropy. In: 2019 International Conference on Robotics and Automation (ICRA), vol. 2019-May, pp. 7844–7850. IEEE (2019). https://doi.org/10.1109/ICRA.2019.8793773
Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1(1), 1–47 (1991). https://doi.org/10.1093/cercor/1.1.1
Gardner, E.P.: Somatosensory cortical mechanisms of feature detection in tactile and kinesthetic discrimination. Can. J. Physiol. Pharmacol. 66(4), 439–454 (1988). https://doi.org/10.1139/y88-074
Gibson, J.J.: Observations on active touch. Psychol. Rev. 69(6), 477–491 (1962). https://doi.org/10.1037/h0046962
Iwamura, Y., Iriki, A., Tanaka, M.: Bilateral hand representation in the postcentral somatosensory cortex. Nature 369(6481), 554–556 (1994). https://doi.org/10.1038/369554a0
Iwamura, Y., Tanaka, M., Sakamoto, M., Hikosaka, O.: Rostrocaudal gradients in the neuronal receptive field complexity in the finger region of the alert monkey’s postcentral gyrus. Exp. Brain Res. 92(3), 360–368 (1993). https://doi.org/10.1007/BF00229023
Jamali, N., Ciliberto, C., Rosasco, L., Natale, L.: Active perception: building objects’ models using tactile exploration. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 179–185. IEEE (2016). https://doi.org/10.1109/HUMANOIDS.2016.7803275
Kappassov, Z., Corrales, J.A., Perdereau, V.: Tactile sensing in dexterous robot hands - review. Robot. Auton. Syst. 74, 195–220 (2015). https://doi.org/10.1016/j.robot.2015.07.015
Lederman, S.J., Klatzky, R.L.: Haptic perception: a tutorial. Attent. Percept. Psychophys. 71(7), 1439–1459 (2009). https://doi.org/10.3758/APP.71.7.1439
Lederman, S.J., Klatzky, R.L.: Hand movements: a window into haptic object recognition. Cogn. Psychol. 19(3), 342–368 (1987). https://doi.org/10.1016/0010-0285(87)90008-9
Lederman, S.J., Klatzky, R.L.: Extracting object properties through haptic exploration. Acta Psychol. 84(1), 29–40 (1993). https://doi.org/10.1016/0001-6918(93)90070-8
Lepora, N.F.: Biomimetic active touch with fingertips and whiskers. IEEE Trans. Haptics 9(2), 170–183 (2016). https://doi.org/10.1109/TOH.2016.2558180
Lepora, N.F.: Touch, vol. 1. Oxford University Press, Oxford (2018). https://doi.org/10.1093/oso/9780199674923.003.0016
Lepora, N.F., Aquilina, K., Cramphorn, L.: Exploratory tactile servoing with active touch. IEEE Robot. Autom. Lett. 2(2), 1156–1163 (2017). https://doi.org/10.1109/LRA.2017.2662071
Li, Q., Kroemer, O., Su, Z., Veiga, F.F., Kaboli, M., Ritter, H.J.: A review of tactile information: perception and action through touch. IEEE Trans. Robot. 36(6), 1619–1634 (2020). https://doi.org/10.1109/TRO.2020.3003230
Martinez-Hernandez, U., Dodd, T., Prescott, T.J., Lepora, N.F.: Active Bayesian perception for angle and position discrimination with a biomimetic fingertip. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5968–5973. IEEE (2013). https://doi.org/10.1109/IROS.2013.6697222
Martinez-Hernandez, U., Dodd, T.J., Natale, L., Metta, G., Prescott, T.J., Lepora, N.F.: Active contour following to explore object shape with robot touch. In: 2013 World Haptics Conference (WHC), pp. 341–346. IEEE (2013). https://doi.org/10.1109/WHC.2013.6548432
Matsubara, T., Shibata, K.: Active tactile exploration with uncertainty and travel cost for fast shape estimation of unknown objects. Robot. Auton. Syst. 91, 314–326 (2017). https://doi.org/10.1016/j.robot.2017.01.014
Prescott, T.J., Diamond, M.E., Wing, A.M.: Active touch sensing. Philos. Trans. R. Soc. B: Biol. Sci. 366(1581), 2989–2995 (2011). https://doi.org/10.1098/rstb.2011.0167
Saal, H.P., Bensmaia, S.J.: Touch is a team effort: interplay of submodalities in cutaneous sensibility. Trends Neurosci. 37(12), 689–697 (2014). https://doi.org/10.1016/j.tins.2014.08.012
Seminara, L., Gastaldo, P., Watt, S.J., Valyear, K.F., Zuher, F., Mastrogiovanni, F.: Active haptic perception in robots: a review. Front. Neurorobot. 13, 53 (2019). https://doi.org/10.3389/fnbot.2019.00053
Yi, Z., et al.: Active tactile object exploration with Gaussian processes. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 2016-Novem, pp. 4925–4930. IEEE (10 2016). https://doi.org/10.1109/IROS.2016.7759723
Zhang, H.: The optimality of Naive Bayes. In: Barr, V., Markov, Z. (eds.) Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004, Florida, vol. 2, pp. 562–567 (2004). https://aaai.org/Library/FLAIRS/2004/flairs04-097.php
Acknowledgments
This work is supported by European Union’s Horizon 2020 MSCA Programme under Grant Agreement No. 813713 NeuTouch.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Salazar, P.J., Prescott, T.J. (2022). Tactile and Proprioceptive Online Learning in Robotic Contour Following. In: Pacheco-Gutierrez, S., Cryer, A., Caliskanelli, I., Tugal, H., Skilton, R. (eds) Towards Autonomous Robotic Systems. TAROS 2022. Lecture Notes in Computer Science(), vol 13546. Springer, Cham. https://doi.org/10.1007/978-3-031-15908-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-15908-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15907-7
Online ISBN: 978-3-031-15908-4
eBook Packages: Computer ScienceComputer Science (R0)