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

Hojung Choi^*1, Jun En Low^*1, Tae Myung Huh¹, Seongheon Hong¹, Gabriela Uribe¹, Kenneth Hoffmann¹, Julia Di¹, Tony Chen¹ Andrew Stanley² Mark Cutkosky¹

¹Stanford University ²Meta * Equal Contribution.

Paper Hardware Instructions Code

At the given force range, CoinFT tracks readings from a reference force/torque sensor well.

Abstract

We introduce CoinFT, a capacitive 6-axis force/torque (F/T) sensor that is compact, light, low-cost, and robust with an average mean-squared error of 0.11N for force and 0.84mNm for moment when the input ranges from 0∼10N and 0∼4N 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 desirable features of CoinFT enables various contact-rich robot interactions at a scale, across different embodiment domains including drones, robot end-effectors, and wearable haptic devices. We demonstrate the utility of CoinFT on drones by performing an attitude-based force control to perform tasks that require careful contact force modulation.

Coinft is robust in the compressive direction

CoinFT Applications

Dexterous Manipulation

In-the-Wild Compliant Manipulation with UMI-FT

CoinFTs on each finger of a modified UMI device is used to collect multimodal data and perform compliance control on both external contact forces and internal grasp forces during manipulation.

Project Website

DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation

CoinFTs are mounted on the fingers of a dexterous hand and are used to teach robots forceful dexterous manipulation through kinesthetic teaching.

Project Website

MuSe multisensory continual learning with CoinFT

Multisensory Continual Learning: Adapting Pretrained Visuomotor Policies to Force

CoinFT provides the force/torque signals that MuSe uses to add force as a new modality to pretrained visuomotor policies during finetuning, improving contact-rich tasks such as wiping and peg insertion while retaining pretrained skills.

Project Website

Drones

Contact Force Control of Aerial Vehicles

A attitude-based force PID control enables aerial vehicles to regulate contact force using CoinFT, allowing drones to perform contact-based tasks reliably.

Wearable Devices

Fourigami: A 4-Degree-of-Freedom, Force-Controlled, Origami, Finger Pad Haptic Device

A 4-DoF fingertip haptic device can provide force-controlled haptic feedback using CoinFT.

Paper

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

CoinFT is used to compare intensity perception of normal and shear forces through a haptic bracelet.

Paper

Design and Evaluation of a 3-DoF Haptic Device for Directional Shear Cues on the Forearm

A 3-DoF forearm-mounted haptic device can provide force-controlled haptic feedback using CoinFT.

Paper

Medical Robotics

PVFT multimodal palpation sensor with CoinFT

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

PVFT pairs a vision-based tactile dome with CoinFT to capture tactile deformation and contact force simultaneously, enabling robotic palpation to detect subsurface tendon features under high loads.

Paper

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CoinFT Maintenance and Support

Hojung Choi

Seongheon Hong

Mark Cutkosky

CoinFT continues to be researched and developed by Hojung Choi, Seongheon Hong, and Mark Cutkosky at the Biomimetics and Dexterous Manipulation Laboratory (BDML) at Stanford University.

For questions and feedback, please email Hojung (ilovehjb0520@gmail.com) and Seongheon (wbfprp@stanford.edu).

BibTeX

@misc{choi2025coinftcoinsizedcapacitive6axis,
      title={CoinFT: A Coin-Sized, Capacitive 6-Axis Force Torque Sensor for Robotic Applications}, 
      author={Hojung Choi and Jun En Low and Tae Myung Huh and Gabriela A. Uribe and Seongheon Hong and Kenneth A. W. Hoffman and Julia Di and Tony G. Chen and Andrew A. Stanley and Mark R. Cutkosky},
      year={2025},
      eprint={2503.19225},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2503.19225}}