This article proposes a method for improving the resolution of a capacitive-type (C-type) torque sensor. This method amplifies the output signal variation of a capacitance-to-digital (CDC) chip by designing a series-resonant circuit. This study considers the data sampling method of the CDC chip and the effects of the resonant circuit on the output signal. The CDC chip employs a switched-capacitor (SC) integrator for data sampling. The SC integrator was simulated to estimate the amplification of the output signal by the resonant circuit. The inductance and resistance of the resonant circuit are designed to optimize the sensor performance. As the SC integrator is commonly used in CDC chips, the proposed method is adaptable to a majority of existing C-type sensors. When fabricating the sensor, it is important to obtain the designed capacitance for implementing the resonant circuit. We design printed circuit board structures that can accurately obtain the designed gap between sensor electrodes and the ground. Artificial neural network is used for sensor calibration process. To prove the effectiveness of these methods, the performance of a sensor equipped with the resonant circuit was compared to that of a previously developed sensor.

A capacitive six-axis force/torque (F/T) sensor has favorable characteristics for miniature design. However, when designing small-sized force/torque sensors, anisotropy among the six axes can lead to uneven sensitivity across each axis. This is due to increased crosstalk errors, which degrade sensor performance. To design a miniature six-axis force/torque sensor, it is essential to analyze the isotropic relationships between the six-axis forces/torques and the capacitance change to reduce crosstalk errors. This paper presents a miniature capacitive six-axis F/T sensor optimized for isotropy. It also establishes a systematic method for designing sensing electrodes. The sensor’s deformable structure is analyzed using Castigliano’s beam theory, and design parameters are optimized with isotropy analysis of the deformable part. The criteria are also presented, including selecting the electrode area and initial gap using linear equations derived from capacitance change analysis. The optimized miniature F/T sensor is calibrated using a neural network-based calibration method, and its accuracy errors are compared to a reference sensor. The design framework provides a foundation for future developments in miniature sensors.

Capacitive sensors are widely adopted in compact robotic systems due to their simple structure, ease of fabrication, and scalability for miniaturized designs. However, sensor miniaturization inevitably leads to reduced sensitivity and increased sensitivity imbalance, particularly in torque measurements, due to limited electrode area and spatial constraints. To address these limitations, this paper presents a compact six-axis force/torque (F/T) sensor based on a redundant capacitive sensing architecture. The proposed sensing architecture employs a symmetric arrangement of multiple capacitive electrodes, providing redundant capacitance measurements that enhance sensitivity while reducing coupling errors under multi-axis loading conditions. By exploiting redundant capacitive responses rather than relying on complex mechanical separation, the proposed design effectively improves measurement robustness. Based on this architecture, a compact six-axis F/T sensor with a diameter of 20 mm and a height of 12 mm is developed. Experimental validation demonstrates that the proposed sensor achieves linearity (>98.2%) with reduced cross-axis interference, confirming improved sensitivity and reliable multi-axis F/T measurement. This work provides a practical and scalable solution for integrating high-performance six-axis F/T sensing into space-constrained robotic systems.

Modern manufacturing is rapidly shifting toward high-mix, low-volume (HMLV) production, which requires flexible and intuitive robot programming methods. However, conventional approaches such as teach pendants or external trackers remain complex and inefficient for non-expert users. This paper presents a hand-gesture based tracking and teaching framework that enables intuitive and no-code robot programming for industrial applications. The system estimates a stable local coordinate frame from robust hand-joint landmarks and employs visual servoing with an eye-in-hand RGB-D camera to continuously and stably track the user’s hand. A finger padding filter is introduced to enhance depth estimation at the fingertips, ensuring reliable 3D point extraction for trajectory definition. On top of low-level pose tracking, a gesture recognition model combined with a finite state machine allows non-expert users to design diverse robot programs entirely through hand gestures, without coding. The proposed framework was first validated through controlled experiments, demonstrating improved teaching accuracy and reduced errors compared to a fixed-camera baseline. Its practical utility was then evaluated in a representative application scenario, where the complete workflow of a surface finishing task such as polishing was carried out solely via hand gestures. A usability study further confirmed its effectiveness, showing high user acceptance and reduced workload. Overall, the framework provides an accessible and generalizable interface for robot programming, supporting flexible deployment in high-mix, low-volume manufacturing.

This paper presents a fuzzy-based adaptive admittance control (FAAC) framework for position-controlled robots in uncertain contact environments. The proposed FAAC regulates admittance parameters using three fuzzy adaptation maps rather than directly generating robot control inputs. The Mass-Adaptation Fuzzy Map (MAFM) adjusts the dominant virtual mass eigenvalue, the Damper–Mass Ratio Fuzzy Map (DMRFM) adapts the damping-related ratio, and the Rendering-Quality Supervisory Fuzzy Map (RQ-SFM) restricts unsafe low-mass adaptation based on rendering quality and vibration metrics. An energy-tank-based admissibility filter is integrated to preserve passivity during online parameter adaptation and contact transitions. Comparative simulations against a stiffness-adaptive baseline and an ablated mass–damping adaptive baseline under nominal, noisy, and filtered sensing conditions verify the robustness of the proposed architecture. Experiments on a UR10 polishing task further show that the proposed FAAC improves force-tracking consistency and contact-maintenance robustness compared with fixed-parameter AAC baselines and FAAC-M. In particular, the proposed FAAC achieved the lowest force standard deviation of 2.76 N and no contact-loss events, whereas the baseline AAC controllers exhibited force fluctuations associated with abrupt desired stiffness changes during contact. These results demonstrate the effectiveness of FAAC for robust robot–environment interaction under uncertain contact conditions.