GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

Donlon, Elliott; Dong, Siyuan; Liu, Melody; Li, Jianhua; Adelson, Edward H.; Rodríguez, Alberto

doi:10.1109/iros.2018.8593661

Cited by 265 publications

(181 citation statements)

References 25 publications

Supporting

Mentioning

180

Contrasting

Unclassified

Order By: Relevance

“…In this paper, we propose a box-packing strategy with a vision-based high-resolution tactile sensor GelSlim [2]. The contributions of the work are: 1) by detecting incipient slip in the packing process with GelSlim, the robot can avoid hard collision with the environment objects.…”

Section: Discussionmentioning

confidence: 99%

“…GelSlim sensor To detect the motion of the object during contact, we use the GelSlim [2] vision-based tactile sensor (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

“…To address the limitations of vision systems, we propose a packing strategy based on high-resolution tactile information. In particular, we use GelSlim [2], a sensor capable of This work was supported by the Amazon Research Awards, and the Toyota Research Institute (TRI). This article solely reflects the opinions and conclusions of its authors and not Amazon or Toyota.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tactile-Based Insertion for Dense Box-Packing

Dong

Rodríguez

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

We study the problem of using high-resolution tactile sensors to control the insertion of objects in a boxpacking scenario. In this paper, we propose an insertion strategy that leverages tactile sensing to: 1) safely probe the box with the grasped object while monitoring incipient slip to maintain a stable grasp on the object. 2) estimate and correct for residual position uncertainties to insert the object into a designated gap without disturbing the environment.Our proposed methodology is based on two neural networks that estimate the error direction and error magnitude, from a stream of tactile imprints, acquired by two GelSlim fingers, during the insertion process. The system is trained on four objects with basic geometric shapes, which we show generalizes to four other common objects. Based on the estimated positional errors, a heuristic controller iteratively adjusts the position of the object and eventually inserts it successfully without requiring prior knowledge of the geometry of the object.The key insight is that dense tactile feedback contains useful information with respect to the contact interaction between the grasped object and its environment. We achieve high success rate and show that unknown objects can be inserted with an average of 6 attempts of the probe-correct loop. The method's ability to generalize to novel objects makes it a good fit for box packing in warehouse automation.

show abstract

Section: Discussionmentioning

confidence: 99%

“…GelSlim sensor To detect the motion of the object during contact, we use the GelSlim [2] vision-based tactile sensor (Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tactile-Based Insertion for Dense Box-Packing

Dong

Rodríguez

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this work we use GelSlim [3], a tactile sensor based on Gelsight [34] that provides high resolution tactile imprints in the form of images. The original Gelsight sensor has been used for object localization of small objects [35], to complement a vision-based tracker [36], or recently to recover 3D shapes using also vision and prior shape models [37].…”

Section: Related Workmentioning

confidence: 99%

Tactile Mapping and Localization from High-Resolution Tactile Imprints

Bauzá

Canal

Rodríguez

2019

2019 International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation.Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website web.mit.edu/mcube/research/tactile localization.html.

show abstract

“…High-resolution tactile sensors, such as GelSight [14], GelSlim [20] and FingerVision [21], use cameras to gather large amounts of data over relatively small contact areas. GelSight in particular uses precise internal lighting and photometric stereo algorithms to generate height maps of contacting geometry.…”

Section: Related Workmentioning

confidence: 99%

Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation

Alspach¹,

Hashimoto²,

Kuppuswarny³

et al. 2019

2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)

View full text Add to dashboard Cite

Incorporating effective tactile sensing and mechanical compliance is key towards enabling robust and safe operation of robots in unknown, uncertain and cluttered environments. Towards realizing this goal, we present a lightweight, easy-to-build, highly compliant dense geometry sensor and end effector that comprises an inflated latex membrane with a depth sensor behind it. We present the motivations and the hardware design for this Soft-bubble and demonstrate its capabilities through example tasks including tactile-object classification, pose estimation and tracking, and nonprehensile object manipulation. We also present initial experiments to show the importance of high-resolution geometry sensing for tactile tasks and discuss applications in robust manipulation.

show abstract

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

Cited by 265 publications

References 25 publications

Tactile-Based Insertion for Dense Box-Packing

Tactile-Based Insertion for Dense Box-Packing

Tactile Mapping and Localization from High-Resolution Tactile Imprints

Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation

Contact Info

Product

Resources

About