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We introduce a novel approach that combines tactile estimation and controlfor in-hand object manipulation. By integrating measurements from robotkinematics and an image-based tactile sensor, our framework estimates andtracks object pose while simultaneously generating motion plans to control thepose of a grasped object. This approach consists of a discrete pose estimatorthat uses the Viterbi decoding algorithm to find the most likely sequence ofobject poses in a coarsely discretized grid, and a continuous poseestimator-controller to refine the pose estimate and accurately manipulate thepose of the grasped object. Our method is tested on diverse objects andconfigurations, achieving desired manipulation objectives and outperformingsingle-shot methods in estimation accuracy. The proposed approach holdspotential for tasks requiring precise manipulation in scenarios where visualperception is limited, laying the foundation for closed-loop behaviorapplications such as assembly and tool use. Please see supplementary videos forreal-world demonstration at https://sites.google.com/view/texterity.

TEXterity: Tactile Extrinsic deXterity