Tactile sensing technologies enable robots to interact with the environment in increasingly nuanced and dexterous ways. A significant gap in this domain is the absence of curved tactile sensors, which are essential for performing sophisticated manipulation tasks. In this study, we present DotTip, a tactile fingertip featuring a three-dimensional curved perceptual surface that closely mimics human fingertip morphology. A convolutional neural network-based deep learning framework precisely calculates the contact angles and forces from the sensor tactile images, achieving mean errors of 1.56 ${}^{\circ }$ and 0.28 N, respectively. DotTip's performance is evaluated in real-world tasks, demonstrating its efficacy in tactile servoing, slip prevention, and grasping, along with the more challenging benchmark task of controlling a joystick. These findings demonstrate that DotTip possesses superior 3D tactile sensing capabilities necessary for fine-grained dexterous manipulations compared to its flat counterparts.

Recent advances in robotics have highlighted the importance of multimodal perception for dexterous manipulation in contact-rich environments. Here we present BiTAT, a bimanual tactile-augmented teleoperation system for collecting multimodal human demonstrations and learning manipulation policies. The system integrates custom capacitive tactile sensors into parallel grippers and displays the resulting contact-deformation images to the operator. We evaluated the system in four controlled laboratory tasks: USB removal/insertion, bottle cap unscrewing, cucumber peeling, and toothpaste squeezing. In a pilot repeated-measures study with eight laboratory participants, visual tactile feedback was associated with success-rate increases of 12.5–32.5 percentage points and shorter completion times among successful trials. We further propose a multimodal Diffusion Policy that fuses visual, tactile, and proprioceptive features through a Transformer encoder. In two fixed-layout autonomous tasks, the complete model achieved higher observed success rates than the vision-only baseline, including a 45-percentage-point difference in the 50-demonstration toothpaste-squeezing setting. Together, these results demonstrate the feasibility of the proposed hardware–policy pipeline and suggest that tactile augmentation benefits both human teleoperation and learned manipulation policies in contact-rich tasks.