Bringing tactile sensation to robotic hands will allow for more effective grasping, along with the wide range of benefits of human-like touch. Here we present a 3d-printed, three-fingered tactile robot hand comprising an OpenHand Model~O customized to house a TacTip optical biomimetic tactile sensor in the distal phalanx of each finger. We expect that the grasping capabilities of the Model O combined with the benefits of sophisticated tactile sensing will result in an effective platform -- the tactile Model O (T-MO). Our current T-MO design uses three JeVois machine vision systems, each comprising a miniature camera in the tactile fingertip with a vision processing module in the base of the hand. To evaluate the capabilities of the T-MO, we benchmark its grasping performance using the Gripper Assessment Benchmark on the YCB object set. We then tested its tactile sensing capabilities with two experiments: firstly, tactile object classification on a subset of objects that can be reliably grasped, and secondly, predicting whether a grasp will successfully lift one of these objects under randomly perturbed grasps that sometimes fail. In all cases, the results are consistent with the state-of-the-art, taking advantage of advances in deep learning and convolutional neural networks from computer vision that apply to the tactile image outputs. Overall, this work demonstrates that the T-MO is an effective platform for robot hand research and we expect it to open-up a range of applications in autonomous object handling. Video: https://youtu.be/oZ41U5pyK6Y
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