# A Sensorized Multicurved Robot Finger with Data-driven Touch Sensing via Overlapping Light Signals

arXiv: https://arxiv.org/abs/2004.00685 · HTML: https://mlsanity.com/paper/2004.00685/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors](https://mlsanity.com/paper/2203.00798/) (2022)
- [A soft thumb-sized vision-based sensor with accurate all-round force perception](https://mlsanity.com/paper/2111.05934/) (2021)
- [TANDEM3D: Active Tactile Exploration for 3D Object Recognition](https://mlsanity.com/paper/2209.08772/) (2022)
- [Tactile-based Object Retrieval From Granular Media](https://mlsanity.com/paper/2402.04536/) (2024)
- [DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation](https://mlsanity.com/paper/2509.18830/) (2025)
- [R×R: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training](https://mlsanity.com/paper/2401.15484/) (2024)
- [TouchAI: Exploring human-AI perceptual alignment in touch through language model representations](https://mlsanity.com/paper/2406.06587/) (2024)
- [NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensors](https://mlsanity.com/paper/2603.09319/) (2026)
- [The Role of Tactile Sensing for Learning Reach and Grasp](https://mlsanity.com/paper/2502.20367/) (2025)

## Abstract (arXiv)

Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this paper, we present a multicurved robotic finger with accurate touch localization and normal force detection over complex, three-dimensional surfaces. The key to our approach is the novel use of overlapping signals from light emitters and receivers embedded in a transparent waveguide layer that covers the functional areas of the finger. By measuring light transport between every emitter and receiver, we show that we can obtain a very rich signal set that changes in response to deformation of the finger due to touch. We then show that purely data-driven deep learning methods are able to extract useful information from such data, such as contact location and applied normal force, without the need for analytical models. The final result is a fully integrated, sensorized robot finger, with a low wire count and using easily accessible manufacturing methods, designed for easy integration into dexterous manipulators.