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Grasping tasks have always been challenging for robots, despite recent innovations in vision-based algorithms and object-specific training. If robots are to match human abilities and learn to pick up never-before-seen objects, they must combine vision with tactile sensing. This paper present a novel way to improve robotic grasping: by using tactile sensors and an unsupervised feature-learning approach,...
As technology develops, manufacture process becomes more and more automated using robots. There is demand for high performance tactile sensor which can support robotic grippers in manipulation tasks especially for unstructured flexible objects. Despite the efforts that have been spent, the fabrication process of those functional sensor remains complicated due to their requirement of specialized materials...
Providing a robot with large-scale tactile-sensing capabilities requires the use of design tools, bridging the gap between user requirements and technical solutions. Given a set of functional requirements (e.g., minimum spatial sensitivity or minimum detectable force), two prerequisites must be considered: 1) the capability of the chosen tactile technology to satisfy these requirements from a technical...
In the process of covering a generic robot with artificial skin, it is necessary to use design tools allowing designers to specify and validate tactile requirements for the scenario at hand. In particular, given a set of well-defined functional requirements (e.g., minimum spatial sensitivity or minimum force to detect), there are two needs to be fulfilled: (i) to check the artificial skin capability...
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