GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper
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Abstract (arXiv)
To adapt to constantly changing environments and be safe for human interaction, robots should have compliant and soft characteristics as well as the ability to sense the world around them. Even so, the incorporation of tactile sensing into a soft compliant robot, like the Fin Ray finger, is difficult due to its deformable structure. Not only does the frame need to be modified to allow room for a vision sensor, which enables intricate tactile sensing, the robot must also retain its original mechanically compliant properties. However, adding high-resolution tactile sensors to soft fingers is difficult since many sensorized fingers, such as GelSight-based ones, are rigid and function under the assumption that changes in the sensing region are only from tactile contact and not from finger compliance. A sensorized soft robotic finger needs to be able to separate its overall proprioceptive changes from its tactile information. To this end, this paper introduces the novel design of a GelSight Fin Ray, which embodies both the ability to passively adapt to any object it grasps and the ability to perform high-resolution tactile reconstruction, object orientation estimation, and marker tracking for shear and torsional forces. Having these capabilities allow soft and compliant robots to perform more manipulation tasks that require sensing. One such task the finger is able to perform successfully is a kitchen task: wine glass reorientation and placement, which is difficult to do with external vision sensors but is easy with tactile sensing. The development of this sensing technology could also potentially be applied to other soft compliant grippers, increasing their viability in many different fields.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Apr 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.
9 papers.
- 2024Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Taskscited by 44
- 2024GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMscited by 20
- 2023GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensingcited by 10
- 2025Sensor-Invariant Tactile Representationcited by 8
- 2025GelBelt: A Vision-based Tactile Sensor for Continuous Sensing of Large Surfacescited by 2
- 2025VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulationcited by 1
- 2023Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensorcited by 0
- 2025A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensorscited by 0
- 2026VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarioscited by 0
- 2026VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios16 Jul 2026
- 2025VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulation26 Nov 2025
- 2025A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensors20 Apr 2025
- 2025Sensor-Invariant Tactile Representation27 Feb 2025
- 2025GelBelt: A Vision-based Tactile Sensor for Continuous Sensing of Large Surfaces9 Jan 2025
- 2024GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs15 Dec 2024
- 2024Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks19 Jun 2024
- 2023Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor12 Oct 2023
- 2023GelSight Svelte: A Human Finger-shaped Single-camera Tactile Robot Finger with Large Sensing Coverage and Proprioceptive Sensing19 Sep 2023