Camera-based tactile sensors are emerging as a promising inexpensive solution for tactile-enhanced manipulation tasks. A recently introduced FingerVision sensor was shown capable of generating reliable signals for force estimation, object pose estimation, and slip detection. In this paper, we build upon the FingerVision design, improving already existing control algorithms, and, more importantly, expanding its range of applicability to more challenging tasks by utilizing raw skin deformation data for control. In contrast to previous approaches that rely on the average deformation of the whole sensor surface, we directly employ local deviations of each spherical marker immersed in the silicone body of the sensor for feedback control and as input to learning tasks. We show that with such input, substances of varying texture and viscosity can be distinguished on the basis of tactile sensations evoked while stirring them. As another application, we learn a mapping between skin deformation and force applied to an object. To demonstrate the full range of capabilities of the proposed controllers, we deploy them in a challenging architectural assembly task that involves inserting a load-bearing element underneath a bendable plate at the point of maximum load.

The research presented in this paper explores a novel tactile sensor technology for architectural assembly tasks. In order to enable robots to interact both with humans and building elements, several robot control strategies had to be implemented. Therefore, we developed a communication interface between the architectural design environment, a tactile sensor and robot controllers. In particular, by combining tactile feedback with real-time gripper and robot control algorithms, we demonstrate grasp adaptation, object shape and texture estimation, slip and contact detection, force and torque estimation. We investigated the integration of robotic control strategies for human-robot interaction and developed an assembly task in which the robot had to place vertical elements underneath a deformed slab. Finally, the proposed tactile feedback controllers and learned skills are combined together to demonstrate applicability and utility of tactile sensing in collaborative human-robot architectural assembly tasks. Users were able to hand over building elements to the robot or guide the robot through the interaction with building elements. Ultimately this research aims to offer the possibility for anyone to interact with built structures through robotic augmentation.

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