Contact force estimation from flexible tactile sensor values considering hysteresis by Gaussian process

Flexible tactile sensors are important elements for facilitating the physical interaction between robots and uncertain environments. For instance, tactile information is used by the robot to grasp objects and interact with humans. A model-based approach is one technique for building a relationship between tactile sensor values and task-relevant information such as force, slip, and temperature. However, it is difficult to create models of flexible tactile sensors for converting sensor signals beforehand due to a nonlinear relation between a contact and the deformations of the flexible form caused by its hysteresis [1]. In contrast, machine learning techniques can be adopted to represent these relationships. For example, Tada et al. [2] proposed a model to acquire the relationship between tactile sensor values and slip vibration using a neural network. The purpose of this study was to develop computational models for learning the association between the force applied to a tactile sensor and the sensor value by compensating for the hysteresis in the sensor. We used the tactile sensor of an iCub fingertip in order to apply our models to cognitive studies. This paper first presents our proposed models that consider a Markov property of taxel (tactile sensor elements) values, and then reports experimental results.