Modeling Target-Distractor Discrimination for Haptic Search in a 3D Environment

摘要

The ability to discriminate between target and distractors, using the information perceived by the hand over time, is essential to perform haptic search successfully, be it for a human hand or a suitably sensorized anthropomorphic robot hand. To address the latter, we train a binary classifier to perform this discrimination during unconstrained haptic search performed by sighted study participants who were blindfolded. In this work, we test different representational concepts and compare the results with the human classification performance. This approach both guides our understanding of human haptic interaction with the 3D environment and aids future modeling of artificial touch for anthropomorphic robot hands. Our contribution is three-fold. Firstly, we are able to acquire a synchronized multimodal time series of exceptionally high spatio-temporal resolution of both the 3D environment and the hand with our novel experimental setup. It includes our Modular Haptic Stimulus Board to represent a 3D environment and a novel tactile glove equipped with position tracking markers and joint angle sensors. Secondly, we introduce a machine learning approach inspired by a novel application of the feature guidance concept for vision (Wolfe et al., 2007 [1]) to modeling of haptic search in a 3D environment, focusing on the target-distractor discrimination. Finally, we compare results for two different types of artificial neural networks, a feedforward and a recurrent network. We show that using recurrent networks, and therefore integrating information over time, improves the classification results. The evaluation also shows that classification accuracy is the highest for the combination of both the tactile and the joint angle modalities.