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In this paper we base upon capacitive tactile proximity sensor modules developed in a previous work to demonstrate applications for safe human-robot-interaction. Arranged as a matrix, the modules can be used to model events in the near proximity of the robot surface, closing the near field perception gap in robotics. The central application investigated here is object tracking. Several results are shown: the tracking of two human hands as well as the handling of occlusions and the prediction of collision for object trajectories. These results are important for novel pretouch-and touch-based humanrobot interaction strategies and for assessing and implementing safety capabilities with these sensor systems.

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Specialized grippers used in the industry are often restricted to specific tasks and objects. However, with the development of dexterous grippers, such as humanoid hands, in-hand pose estimation becomes crucial for successful manipulations, since objects will change their pose during and after the grasping process. In this paper, we present a gripping system and describe a new pose estimation algorithm based on tactile sensory information in combination with haptic rendering models (HRMs). We use a 3-finger manipulator equipped with tactile force sensing elements. A particle filter processes the tactile measurements from these sensor elements to estimate the grasp pose of an object. The algorithm evaluates hypotheses of grasp poses by comparing tactile measurements and expected tactile information from CAD-based haptic renderings, where distance values between the sensor and 3D-model are converted to forces. Our approach compares the force distribution instead of absolute forces or distance values of each taxel. The haptic rendering models of the objects allow us to estimate the pose of soft deformable objects. In comparison to mesh-based approaches, our algorithm reduces the calculation complexity and recognizes ambiguous and geometrically impossible solutions.