In this work, we present an active tactile perception approach for contour following based on a probabilistic framework. Tactile data were collected using a biomimetic fingertip sensor. We propose a control architecture that implements a perception-action cycle for the exploratory procedure, which allows the fingertip to react to tactile contact whilst regulating the applied contact force. In addition, the fingertip is actively repositioned to an optimal position to ensure accurate perception. The method is trained off-line and then the testing performed on-line based on contour following around several different test shapes. We then implement object recognition based on the extracted shapes. Our active approach is compared with a passive approach, demonstrating that active perception is necessary for successful contour following and hence shape recognition.

Motivated by the impact of superresolution methods for imaging, we undertake a detailed and systematic analysis of localization acuity for a biomimetic fingertip and a flat region of tactile skin. We identify three key factors underlying superresolution that enable the perceptual acuity to surpass the sensor resolution: 1) the sensor is constructed with multiple overlapping, broad but sensitive receptive fields; 2) the tactile perception method interpolates between receptors (taxels) to attain subtaxel acuity; and 3) active perception ensures robustness to unknown initial contact location. All factors follow from active Bayesian perception applied to biomimetic tactile sensors with an elastomeric covering that spreads the contact over multiple taxels. In consequence, we attain extreme superresolution with a 35-fold improvement of localization acuity (0.12 mm) over sensor resolution (4 mm). We envisage that these principles will enable cheap high-acuity tactile sensors that are highly customizable to suit their robotic use. Practical applications encompass any scenario where an end-effector must be placed accurately via the sense of touch.Index Terms-Biomimetics, force and tactile sensing.

Please cite this article as: U. Martinez-Hernandez, et al., Active sensorimotor control for tactile exploration, Robotics and Autonomous Systems (2016), http://dx.doi.org/10. 1016/j.robot.2016.09.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Active sensorimotor control for tactile exploration AbstractIn this paper, we present a novel and robust Bayesian approach for autonomous active exploration of unknown objects using tactile perception and sensorimotor control. Despite recent advances in tactile sensing, robust active exploration remains a challenging problem, which is a major hurdle to the practical deployment of tactile sensors in robots.Our proposed approach is based on a Bayesian perception method that actively controls the sensor with local small repositioning movements to reduce perception uncertainty, followed by explorative movements based on the outcome of each perceptual decision making step. Two sensorimotor control strategies are proposed for improving the accuracy and speed of the active exploration that weight the evidence from previous exploratory steps through either a weighted prior or weighted posterior. The methods are validated both off-line and in real-time on a contour following exploratoryprocedure. Results clearly demonstrate improvements in both accuracy and exploration time when using the proposed active methods compared to passive perception. Our work demonstrates that active perception has the potential to enable robots to perform robust autonomous tactile exploration in natural environments.

Abstract-In this paper, we propose that active Bayesian perception has a general role for Simultaneous Object Localization and IDentification (SOLID), or deciding where and what. We test this claim using a biomimetic fingertip to perceive object identity via surface shape at uncertain contact locations. Our method for active Bayesian perception combines decision making by threshold crossing of the posterior belief with a sensorimotor loop that actively controls sensor location based on those beliefs. Our findings include: (i) active perception with a fixation control strategy gives an order-of-magnitude improvement in acuity over passive perception without sensorimotor feedback; (ii) perceptual acuity improves as the active control requires less belief to make a relocation decision; and (iii) relocation noise further improves acuity. The best method has aspects that resemble animal perception, supporting wide applicability of these findings.

In this paper, we propose that active perception will help attain autonomous robotics in unstructured environments by giving robust perception. We test this claim with a biomimetic fingertip that senses surface texture under a range of contact depths. We compare the performance of passive Bayesian perception with a novel approach for active perception that includes a sensorimotor loop for controlling sensor position. Passive perception at a single depth gave poor results, with just 0.2 mm uncertainty impairing performance. Extending passive perception over a range of depths gave non-robust performance. Only active perception could give robust, accurate performance, with the sensorimotor feedback compensating the position uncertainty. We expect that these results will extend to other stimuli, so that active perception will offer a general approach to robust perception in unstructured environments.