Abstract
It has been previously demonstrated in robots that the mimicking of functional characteristics of biologic memory can be beneficial for providing accurate learning and recognition in circumstances of social human-robot-interaction. The effective encoding of social and physical salient features has been demonstrated through the use of Bayesian Latent Variable Models as abstractions of memories (Simple Synthetic Memories). In this work, we explore the capabilities of formation and recall of tactile memories associated to the encoding of geometric and spatial qualities. Compression and pattern separation are evaluated against the use of raw data in a nearest neighbour regression model, obtaining a substantial improvement in accuracy for prediction of geometric properties of the stimulus. Additionally, pattern completion is assessed with the generation of ‘imagined touch’ streams of data showing similarities to real world tactile observations. The use of this model for tactile memories offers the potential for robustly perform sensorimotor tasks in which the sense of touch is involved.
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Acknowledgments
This work was supported by European Union’s Horizon 2020 MSCA Programme under Grant Agreement No. 813713 NeuTouch and by the EU Horizon 2020 FET Flagship programme through the Human Brain Project (HBP-SGA3, 945539).
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TJP is a director and shareholder in two robotics companies-Consequential Robotics Ltd. and Bettering Our Worlds (BOW) Ltd. These companies are not expected to benefit from this publication. PJS has no competing interests.
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Salazar, P.J., Prescott, T.J. (2023). Simple Synthetic Memories of Robotic Touch. In: Meder, F., Hunt, A., Margheri, L., Mura, A., Mazzolai, B. (eds) Biomimetic and Biohybrid Systems. Living Machines 2023. Lecture Notes in Computer Science(), vol 14157. Springer, Cham. https://doi.org/10.1007/978-3-031-38857-6_1
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