Digger Finger: GelSight Tactile Sensor for Object Identification Inside Granular Media

Patel, Radhen; Ouyang, Rui; Romero, Branden; Adelson, Edward H.

doi:10.1007/978-3-030-71151-1_10

Cited by 16 publications

(10 citation statements)

References 9 publications

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“…A short strip of TPU filament was superglued to its back and along the empty middle section of the finger to hold the blue LEDs in place. Together with the fluorescent paints, the blue LEDs helped to provide a tri-color sensing region, similar to how a GelSight wedge and digger fingers are illuminated [24], [25]. The presence of all three colors help the sensor to perform depth reconstruction of objects the gripper comes into contact with.…”

Section: Illuminationmentioning

confidence: 99%

GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper

Liu¹,

Adelson²

2022

2022 IEEE 5th International Conference on Soft Robotics (RoboSoft)

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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.

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Section: Illuminationmentioning

confidence: 99%

GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper

Liu¹,

Adelson²

2022

2022 IEEE 5th International Conference on Soft Robotics (RoboSoft)

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“…IV-B. Similarly, the same method can be used to approach the problem of finding hazardous objects concealed or buried, and recognizing their categories by the shape of contours without the need of extracting them out [76].…”

Section: A Broader Applicationsmentioning

confidence: 99%

Active Multi-Object Exploration and Recognition via Tactile Whiskers

Xiao,

Xu,

et al. 2021

Preprint

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Robotic exploration under uncertain environments is challenging when optical information is not available. In this paper, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment non-intrusively.This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery.Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.

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“…In terms of control, vision-based tactiles sensors have been used for tasks such as learning grasp success [2], servoing object surfaces [14], detecting slip and shear force [36], object insertion [25], cable manipulation [29], and object reposing [8]. While in terms of system identification it has been used in reconstructing 3D surfaces [32,30], distinguishing between different cloth materials [37], estimating object pose [1], finding objects in granular media [22], determining material softness [34], and learning physical features to perform dynamic in-hand manipulation [31].…”

Section: Application Of Vision-based Tactile Sensorsmentioning

confidence: 99%

“…With the proliferation of vision-based tactile sensors [15,35,33,26,12] we have seen major advancements in the field of robotics [2,14,36,25,29,8,32,30,37,22,34,31]. However impressive this may be, intelligent behavior extends beyond the brains of any system.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation

Romero

Veiga

Adelson

2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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In this work we introduce a non-planar soft high-resolution tactile sensor. An iteration of the GelSight sensors, it enables future GelSights to have more complicated form factors, such as a humanoid fingertip. To do this we introduce a novel method for achieving directional lighting along the entirety of a curved sensor using light piping. Light piping uses total internal reflection and a semi-specular membrane to constrain the path of the light inside the sensor until the sensing membrane is deformed. By using this new membrane and changing the geometry, we introduce a new bidirectional reflectance distribution function and new optics. This require new calibration procedures in the form of developing a fisheye projection model, and developing a neighborhood and location based continuous look-up table to map the relationship between RGB value and surface normal orientation of the membrane at a point. Finally we perform two dexterous manipulation task with feedback from the sensors in the form of controlled rolling of an object on a support surface, and lid removal off a jar. We also give instructions on how to manufacture the sensor as well as increasing the durability of the membrane for all GelSight sensors.

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Digger Finger: GelSight Tactile Sensor for Object Identification Inside Granular Media

Cited by 16 publications

References 9 publications

GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper

GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant Robotic Gripper

Active Multi-Object Exploration and Recognition via Tactile Whiskers

Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation

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