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This paper presents a novel approach for haptic object recognition with an anthropomorphic robot hand. Firstly, passive degrees of freedom are introduced to the tactile sensor system of the robot hand. This allows the planar tactile sensor patches to optimally adjust themselves to the object's surface and to acquire additional sensor information for shape reconstruction. Secondly, this paper presents an approach to classify an object directly from the haptic sensor data acquired by a palpation sequence with the robot hand -without building a 3d-model of the object. Therefore, a finite set of essential finger positions and tactile contact patterns are identified which can be used to describe a single palpation step. A palpation sequence can then be merged into a simple statistical description of the object and finally be classified. The proposed approach for haptic object recognition and the new tactile sensor system are evaluated with an anthropomorphic robot hand.

This paper presents a multi-sensor based generic approach to opening doors for a dexterous robot. Once the handle has been located by a computer vision algorithm and properly grasped, we are able to open doors without using a model or other prior knowledge of the door geometry. This is done by combining the sensor information of both a force-torque sensor in the robot wrist and a tactile sensor matrix in the robot gripper itself. Our experimental results show that the combination of both sensors achieves the most successful way to open the door.

In this paper tactile proximity sensors for close human-robot interactions based on a previously developed sensor are introduced. Using the same sensing technology, we developed large area tactile proximity sensors as a robot skin and small sensors which we have integrated in an anthropomorphic robot hand. Tactile sensing in the area of robotics for close human interaction is still a challenging task. In the most cases tactile sen sors need to be supported by other sensor modalities to perceive the robots environment before contacts occur. To overcome this issue we developed tactile proximity sensors for robot surfaces and for robot grippers. Both sensors, their behaviour and a model of the tactile sensor will be discussed in this paper.

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.