Magnetic-based tactile sensors (MBTS) combine the advantages of compact design and high-frequency operation but suffer from limited spatial resolution due to their sparse taxel arrays. This paper proposes SuperMag, a tactile shape reconstruction method that addresses this limitation by leveraging high-resolution vision-based tactile sensor (VBTS) data to supervise MBTS super-resolution. Co-designed, open-source VBTS and MBTS with identical contact modules enable synchronized data collection of high-resolution shapes and magnetic signals via a symmetric calibration setup. We frame tactile shape reconstruction as a conditional generative problem, employing a conditional variational auto-encoder to infer high-resolution shapes from low-resolution MBTS inputs. The MBTS achieves a sampling frequency of 125 Hz, whereas the shape reconstruction sustains an inference time within 2.5 ms. This cross-modality synergy advances tactile perception of the MBTS, potentially unlocking its new capabilities in high-precision robotic tasks.

Endowing the curved surfaces of rounded vision-based tactile fingers is essential for dexterous robotic manipulation, as they offer more sufficient contact with the environment. However, current rounded designs are constrained by a low sensing frequency (30–60 Hz) and the need for recalibration when adapting to new sensors due to the reliance on multi-channel captures, which hinders their performance in dynamic robotic tasks and large-scale deployment. In this work, we introduce R-Tac0, a low-cost rounded VBTS engineered for high-resolution and high-speed perception. The key innovation is a monochrome vision-based sensing principle: utilizing a black-and-white camera to capture the reflection properties of the compound rounded elastomer under monochromatic illumination. This single-channel imaging significantly reduces data volume and simplifies computational complexity, enabling 120 Hz tactile perception. A lightweight neural network can calibrate the sensor to achieve a depth reconstruction accuracy of 0.169 mm per pixel, while exhibiting surprisingly good transferability to new sensors. In experiments, we demonstrate the advantages of R-Tac0’s rounded design by evaluating its performance under different contact angles, its high-frequency perception in slip detection, and its effectiveness in robotic dynamic pose estimation.

Integrating robots into human-centric environments such as homes, necessitates advanced manipulation skills as robotic devices will need to engage with articulated objects such as doors and drawers. Key challenges in robotic manipulation of articulated objects are the unpredictability and diversity of these objects' internal structures, which render models based on object kinematics priors, both explicit and implicit, and inadequate. Their reliability is significantly diminished by pre-interaction ambiguities, imperfect structural parameters, encounters with unknown objects, and unforeseen disturbances. Here, we present aprior-freestrategy, Tac-Man, focusing on maintaining stable robot-object contact during manipulation. Without relying on object priors, Tac-Man leverages tactile feedback to enable robots to proficiently handle a variety of articulated objects, including those with complex joints, even when influenced by unexpected disturbances. Demonstrated in both real-world experiments and extensive simulations, it consistently achieves near-perfect success in dynamic and varied settings, outperforming existing methods. Our results indicate that tactile sensing alone suffices for managing diverse articulated objects, offering greater robustness and generalization than prior-based approaches. This underscores the importance of detailed contact modeling in complex manipulation tasks, especially with articulated objects. Advancements in tactile-informed approaches significantly expand the scope of robotic applications in human-centric environments, particularly where accurate models are difficult to obtain.

Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency, (ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

In conjunction with huge recent progress in cam-era and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information.

This paper demonstrates a working prototype for shape sensing using miniature optoelectronic sensors integrated into a chain of rotational links. Wearable sensors for rehabilitation, prosthetics and robotics must be lightweight, miniature, and compact to allow comfortable range of motion without obstruction, and therefore, the integrated network of sensors and hardware must be adapted to this. The sensing principle is based on light intensity modulation using a curvature varying reflector. The modular sensing configuration design offers a low-cost, miniaturized approach to shape sensing, compatible in clinical applications. A prototype is constructed, and calibration is carried out. Shape sensing estimation is evaluated to assess accuracy. A four-link rotational chain prototype shows average estimation errors of 2.4° for shape sensing compared to an inertial measurement unit.

Measuring forces and torques applied to tools of unconventional shapes can be challenging due to difficulties in connecting commercially available force/torque sensors. We propose an innovative, customizable six-axis force/torque sensor which can be clamped around a tool or end-effector to minimize the proximity between tool tip and sensor structure. The sensor is fabricated using 3d printing technology and consists of two pieces which together form an 8-legged Stewart platform. Each leg is designed as a cantilever beam to allow for a measurable displacement under an external load. The displacements of the legs are measured with 8 light intensity-based optoelectronic sensors, which exhibit high sensitivities and low noise levels without the need for external amplification circuitry. A customized printed circuit board and peripheral hardware are proposed to allow for efficient analog-to-digital conversion of the force/torque measurement. A calibration process is proposed which makes use ofa commercial ATI Mini40 sensor and custom hardware to allow for fast calibration routines. Finally, the calibrated sensor design is compared to the ATI Mini40 sensor by measuring sequences of forces and torques, and the maximum errors of force/torque components (Fx, Fy, Fz, Mx, My, Mz) are 16.3%, 20.0%, 27.5%, 20.5%, 21.6%, 14.9% respectively.

In this paper, energy saving of two-tier network: macrocells overlaid by randomly distributed picos is considered. A pico on/off algorithm based on offload bandwidth is proposed to save energy and improve the energy efficiency. Pico on/off selection will be triggered when the traffic load of macro base station (BS) exceeds thresholds. The objective of the algorithm is to minimize the number of active picos. Simulation results show that the proposed algorithm can save more energy compared with state of the art scheme.