arXiv subjects

Tae Myung Huh

Publications and source records attributed to Tae Myung Huh.

8 recordsLinked to original sources

arXiv2026

PinFT: Miniature 5-Axis Force/Torque Sensor Embeddable to Tweezer-like Tool

We present PinFT, a miniature five-axis capacitive force/torque sensor designed for direct tip-level integration into tweezer-like tools. The sensor employs a compact three-PCB stack with segmented plated through-hole electrodes and a silicone elastomer dielectric, enabling five-degree-of-freedom force and torque sensing ($F_x$, $F_y$, $F_z$, $T_x$, $T_y$) through displacement of a central 2\,mm-diameter stainless steel pin. The fabricated prototype was calibrated using a higher-order polynomial mapping, yielding mean absolute errors of approximately 0.23\,N for forces and 2.5\,mN$\cdot$m for torques, with coefficients of determination ($R^2$) exceeding 0.97 across all axes. To demonstrate practical utility, a 3D-printed tweezer integrating PinFT sensors at both tips was mounted on a parallel-jaw gripper and evaluated across three representative manipulation tasks: grasping a sub-millimeter SMD capacitor, pulling a simulated hair from a silicone substrate, and tearing a compliant silicone specimen. In all cases, per-tip force sensing reliably captured characteristic force signatures that distinguish successful manipulation from failure events -- including slip and object ejection -- using gradient-based features derived from internal grasp force and net interaction force. These results demonstrate that direct, per-tip force sensing enables standard parallel-jaw grippers to monitor and interpret fine manipulation tasks performed through a handheld tweezer.

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arXiv2026

Strain in Sound: Soft Corrugated Tube for Local Strain Sensing with Acoustic Resonance

We present a soft corrugated tube sensor designed to estimate strain in each half segment. When air flows through the tube, the internal corrugated cavities induce pressure oscillations that excite the tube's standing wave resonance mode, generating an acoustic tone. Stretching the tube affects both the resonance mode frequency, due to changes in overall length, and the frequency-flow speed relationship, due to variations in cavity width, which is particularly useful for local strain estimation. By sweeping flow rates in a controlled manner, we collected resonance frequency data across flow speeds under various local stretch conditions, enabling a machine learning algorithm (gradient boosting regressor) to estimate segmental strain with high accuracy. The dual-period tube design (3.1 mm and 4.18 mm corrugation periods) achieved a mean absolute error (MAE) of 0.8 mm, while the single-period tube (3.1 mm) provided a satisfactory MAE of 1 mm. Testing on a mannequin finger demonstrated the sensor's capability to differentiate multi-joint configurations, showing its potential for estimating non-uniform deformations in soft bodies.

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arXiv2026

CoinFT: A Coin-Sized, Capacitive 6-Axis Force Torque Sensor for Robotic Applications

We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average root-mean-squared error of 0.16N for force and 1.08mNm for moment when the input ranges from 0~14N and 0~5N in normal and shear directions, respectively. CoinFT is a stack of two rigid PCBs with comb-shaped electrodes connected by an array of silicone rubber pillars. The microcontroller interrogates the electrodes in different subsets in order to enhance sensitivity for measuring 6-axis F/T. The combination of features of CoinFT enables various contact-rich robot interactions across different embodiment domains including drones, robot end-effectors, and wearable haptic devices. We demonstrate the utility of CoinFT through two representative applications: a multi-axial contact-probing experiment in which a CoinFT mounted beneath a hemispherical fingertip measures 6-axes of force and torque representative of manipulation scenarios, and an attitude-based force-control task on a drone. The design, fabrication, and firmware of CoinFT are open-sourced at https://coin-ft.github.io/.

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arXiv2024

Two-Finger Soft Gripper Force Modulation via Kinesthetic Feedback

We investigate a method to modulate contact forces between the soft fingers of a two-finger gripper and an object, without relying on tactile sensors. This work is a follow-up to our previous results on contact detection. Here, our hypothesis is that once the contact between a finger and an object is detected, a controller that keeps a desired difference between the finger bending measurement and its bending at the moment of contact is sufficient to maintain and modulate the contact force. This approach can be simultaneously applied to both fingers while getting in contact with a single object. We successfully tested the hypothesis, and characterized the contact and peak pull-out force magnitude vs. the desired difference expressed by a multiplicative factor. All of the results are performed on a real physical device.

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arXiv2024

Haptic search with the Smart Suction Cup on adversarial objects

Suction cups are an important gripper type in industrial robot applications, and prior literature focuses on using vision-based planners to improve grasping success in these tasks. Vision-based planners can fail due to adversarial objects or lose generalizability for unseen scenarios, without retraining learned algorithms. We propose haptic exploration to improve suction cup grasping when visual grasp planners fail. We present the Smart Suction Cup, an end-effector that utilizes internal flow measurements for tactile sensing. We show that model-based haptic search methods, guided by these flow measurements, improve grasping success by up to 2.5x as compared with using only a vision planner during a bin-picking task. In characterizing the Smart Suction Cup on both geometric edges and curves, we find that flow rate can accurately predict the ideal motion direction even with large postural errors. The Smart Suction Cup includes no electronics on the cup itself, such that the design is easy to fabricate and haptic exploration does not damage the sensor. This work motivates the use of suction cups with autonomous haptic search capabilities in especially adversarial scenarios.

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arXiv2023

Initial Analysis of Data-Driven Haptic Search for the Smart Suction Cup

Suction cups offer a useful gripping solution, particularly in industrial robotics and warehouse applications. Vision-based grasp algorithms, like Dex-Net, show promise but struggle to accurately perceive dark or reflective objects, sub-resolution features, and occlusions, resulting in suction cup grip failures. In our prior work, we designed the Smart Suction Cup, which estimates the flow state within the cup and provides a mechanically resilient end-effector that can inform arm feedback control through a sense of touch. We then demonstrated how this cup's signals enable haptically-driven search behaviors for better grasping points on adversarial objects. This prior work uses a model-based approach to predict the desired motion direction, which opens up the question: does a data-driven approach perform better? This technical report provides an initial analysis harnessing the data previously collected. Specifically, we compare the model-based method with a preliminary data-driven approach to accurately estimate lateral pose adjustment direction for improved grasp success.

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arXiv2022

Perceived Intensities of Normal and Shear Skin Stimuli using a Wearable Haptic Bracelet

Our aim is to provide effective interaction with virtual objects, despite the lack of co-location of virtual and real-world contacts, while taking advantage of relatively large skin area and ease of mounting on the forearm. We performed two human participant studies to determine the effects of haptic feedback in the normal and shear directions during virtual manipulation using haptic devices worn near the wrist. In the first study, participants performed significantly better while discriminating stiffness values of virtual objects when the feedback consisted of normal displacements compared to shear displacements. Participants also commented that they could detect normal cues much easier than shear, which motivated us to perform a second study to find the point of subjective equality (PSE) between normal and shear stimuli. Our results show that shear stimuli require a larger actuator displacement but less force than normal stimuli to achieve perceptual equality for our haptic bracelets. We found that normal and shear stimuli cannot be equalized through skin displacement nor the interaction forces across all users. Rather, a calibration method is needed to find the point of equality for each user where normal and shear stimuli create the same intensity on the user's skin.

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arXiv2021

A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration

We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is applied to a new suction cup design that contains multiple chambers for air flow. Each chamber connects with its own remote pressure transducer, which enables both absolute and differential pressure measures between chambers. By changing the overall vacuum applied to this smart suction cup, it can perform different functions such as gentle haptic exploration (low pressure) and monitoring breaks in the seal during strong astrictive gripping (high pressure). Haptic exploration of surfaces through sliding and palpation can guide the selection of suction grasp locations and help to identify the local surface geometry. During suction gripping, this design localizes breaks in the suction seal between four quadrants with up to 97% accuracy and detects breaks in the suction seal early enough to avoid total grasp failure.

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