Compensation for tactile hysteresis using Gaussian process with sensory Markov property

Flexible tactile sensors have been studied to enable robots to interact with objects in unstructured environments. However, due to nonlinearity caused by the hysteresis of tactile materials, it is difficult to accurately convert sensor signals into task-relevant information such as force and slip. To compensate for the hysteresis of flexible tactile sensors, we propose a model based on a Gaussian process. The key idea of our model is to include the Markov property of sensory input. The proposed model not only uses the current tactile signal, but also its time-series signals, to extract the influence of the past states on the current state. We evaluate the accuracy of force estimation using the proposed model in comparison to the normal Gaussian process model, which does not take the Markov property into account. The experimental results demonstrate that the performance of our model improves on the normal Gaussian process in terms of root mean squared error, correlation coefficient, and absolute maximum error between the actual and the estimated force. We discuss the advantages of accounting for the sensory Markov property and the potential ability of the Gaussian process to internally acquire the representation of the deviation of sensory signals.