Advanced dexterous manipulation requires identifying and controlling multiple simultaneous contacts, including interactions with compliant objects where deformations are large. Raw optical tactile images are information rich but lack calibrated physical meaning, limiting cross-sensor use and real-world deployment. We present TensorTouch, a calibration framework that combines finite element analysis and learning to infer dense deformation and stress/force fields from a single tactile image. Across real sensors, TensorTouch achieves contact localization errors under 1.29 mm and mean force errors under 0.139 N per axis. In a multi-object selective grasp task with two simultaneously contacted objects (including identical cables), the system achieves up to 90.0% success. We further demonstrate robustness under repeated loading, yielding 38.295 dB PSNR between the initial tactile image and the image after 20,000 contacts. The learned model runs in real time at 95 Hz on an RTX 5090, proving to be suitable for contact-rich, dexterous manipulation.

Dexterous manipulation, especially of small daily objects, continues to pose complex challenges in robotics. This paper introduces the DenseTact-Mini, an optical tactile sensor with a soft, rounded, smooth gel surface and compact design equipped with a synthetic fingernail. We propose three distinct grasping strategies: tap grasping using adhesion forces such as electrostatic and van der Waals, fingernail grasping leveraging rolling/sliding contact between the object and fingernail, and fingertip grasping with two soft fingertips. Through comprehensive evaluations, the DenseTact-Mini demonstrates a lifting success rate exceeding 90.2% when grasping various objects, including items such as 1mm basil seeds, thin paperclips, and items larger than 15mm such as bearings. This work demonstrates the potential of soft optical tactile sensors for dexterous manipulation and grasping.

In this work, we propose a novel method to supervise 3D Gaussian Splatting (3DGS) scenes using optical tactile sensors. Optical tactile sensors have become widespread in their use in robotics for manipulation and object representation; however, raw optical tactile sensor data is unsuitable to directly supervise a 3DGS scene. Our representation leverages a Gaussian Process Implicit Surface to implicitly represent the object, combining many touches into a unified representation with uncertainty. We merge this model with a monocular depth estimation network, which is aligned in a two stage process, coarsely aligning with a depth camera and then finely adjusting to match our touch data. For every training image, our method produces a corresponding fused depth and uncertainty map. Utilizing this additional information, we propose a new loss function, variance-weighted depth supervised loss, for training the 3DGS scene model. We leverage the DenseTact optical tactile sensor and RealSense RGB-D camera to show that combining touch and vision in this manner leads to quantitatively and qualitatively better results than vision or touch alone in few-view scene synthesis on opaque, reflective and transparent objects. Please see our project page at armlabstanford.github.io/touchgs.

Collaborative robots stand to have an immense impact on both human welfare in domestic service applications and industrial superiority in advanced manufacturing with dexterous assembly. The outstanding challenge is providing robotic fingertips with a physical design that makes them adept at performing dexterous tasks that require high-resolution, calibrated shape reconstruction and force sensing. In this work, we present DenseTact 2.0, an optical-tactile sensor capable of visualizing the deformed surface of a soft fingertip and using that image in a neural network to perform both calibrated shape reconstruction and 6-axis wrench estimation. We demon-strate the sensor accuracy of 0.3633mm per pixel for shape reconstruction, 0.410N for forces, 0.387N. mm for torques, and the ability to calibrate new fingers through transfer learning, which achieves comparable performance with only 12% of the non-transfer learning dataset size.

Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in manipulation tasks. Existing tactile sensor solutions with high resolution have limitations that include low accuracy, expensive components, or lack of scalability. In this paper, an inexpensive, scalable, and compact tactile sensor with high-resolution surface deformation modeling for surface reconstruction of the 3D sensor surface is presented. By observing the contact surface with a fisheye camera, it is shown that the surface deformation can be estimated in real-time (1.8 ms) using deep convolutional neural networks. This sensor in its design and sensing abilities represents a significant step toward better object in-hand localization, classification, and surface estimation all enabled by calibrated, high-resolution shape reconstruction.