Integration of touch attention mechanisms to improve the robotic haptic exploration of surfaces

This text presents the integration of touch attention mechanisms to improve the efficiency of the action-perception loop, typically involved in active haptic exploration tasks of surfaces by robotic hands. The progressive inference of regions of the workspace that should be probed by the robotic system uses information related with haptic saliency extracted from the perceived haptic stimulus map (exploitation) and a “curiosity”-inducing prioritisation based on the reconstruction's inherent uncertainty and inhibition-of-return mechanisms (exploration), modulated by top-down influences stemming from current task objectives, updated at each exploration iteration. This work also extends the scope of the top-down modulation of information presented in a previous work, by integrating in the decision process the influence of shape cues of the current exploration path. The Bayesian framework proposed in this work was tested in a simulation environment. A scenario made of three different materials was explored autonomously by a robotic system. The experimental results show that the system was able to perform three different haptic discontinuity following tasks with a good structural accuracy, demonstrating the selectivity and generalization capability of the attention mechanisms. These experiments confirmed the fundamental contribution of the haptic saliency cues to the success and accuracy of the execution of the tasks.