arXiv subjects

Seongheon Hong

Publications and source records attributed to Seongheon Hong.

5 recordsLinked to original sources

arXiv2026

TacClip: a clip-on sensor measures dynamic contact forces without covering the fingerpads

TacClip is a minimally encumbering wearable device for recording fingertip deformation caused by contact forces and vibrations. It can be combined with vision- or glove-based hand tracking systems that leave the fingertips uncovered and provides a measure of dynamic contact interactions, while leaving the finger pads exposed so that the user retains natural sensitivity to texture, friction, temperature, and fine surface features. The signal is produced by a Fiber Bragg Grating (FBG) embedded on a small plastic clip mounted over the fingernail. Optionally, for use with vision-based tracking, additional FBGs on polyimide strips can complement camera-based pose estimation. In finger pressing tests, TacClip estimates the force magnitude with typical errors below $0.5~\mathrm{N}$ over a $0$--$8~\mathrm{N}$ range. In tests of cloth handling and tape edge finding, we show that it captures the vibrations and dynamic events generated during exploratory sliding. With no electronics, TacClip can also be used submerged in water, while preserving bare finger contact.

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arXiv2026

Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force

Robot manipulation often relies on sensory feedback beyond vision, particularly in contact-rich settings where force, tactile, or audio signals reveal interaction states that are not directly observable from images. However, these modalities are often hardware- and task-specific, and large-scale multisensory robot datasets remain scarce. As a result, it is impractical to pretrain policies with every sensor they may encounter. We study multisensory continual learning: adapting a pretrained robot policy to new tasks with newly introduced modalities while preserving performance under the original sensor suite. We propose MultiSensory World Model (MuSe), which incorporates limited multisensory data into pretrained vision-only policies through multi-stage fusion, multisensory future prediction, and experience replay over pretraining data. We instantiate MuSe by augmenting a pretrained vision-only policy with force-torque sensing and evaluate it on real-world manipulation tasks. Our experiments show that MuSe performs strongly on contact-rich finetuning tasks while preserving, and in some cases improving, performance on the original pretraining tasks. These results suggest that a modest multisensory dataset can improve general robot capabilities beyond the finetuning distribution. Project website: https://jadenvc.github.io/multisensory-continual-learning/

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arXiv2026

In-the-Wild Compliant Manipulation with UMI-FT

Many manipulation tasks require careful force modulation. With insufficient force the task may fail, while excessive force could cause damage. The high cost, bulky size and fragility of commercial force/torque (F/T) sensors have limited large-scale, force-aware policy learning. We introduce UMI-FT, a handheld data-collection platform that mounts compact, six-axis force/torque sensors on each finger, enabling finger-level wrench measurements alongside RGB, depth, and pose. Using the multimodal data collected from this device, we train an adaptive compliance policy that predicts position targets, grasp force, and stiffness for execution on standard compliance controllers. In evaluations on three contact-rich, force-sensitive tasks (whiteboard wiping, skewering zucchini, and lightbulb insertion), UMI-FT enables policies that reliably regulate external contact forces and internal grasp forces, outperforming baselines that lack compliance or force sensing. UMI-FT offers a scalable path to learning compliant manipulation from in-the-wild demonstrations. We open-source the hardware and software to facilitate broader adoption at:https://umi-ft.github.io/.

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arXiv2025

Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation

Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact multimodal sensor that integrates high-resolution vision-based tactile imaging with a 6-axis force-torque sensor. In experiments on silicone phantoms with diverse subsurface tendon geometries, force signals alone frequently produce ambiguous responses, while tactile images reveal clear structural differences in presence, diameter, depth, crossings, and multiplicity. Yet accurate force tracking remains essential for maintaining safe, consistent contact during physiotherapeutic interaction. Preliminary results show that combining tactile and force modalities enables robust subsurface feature detection and controlled robotic palpation.

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arXiv2025

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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