Learning-Based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping

Yang, Linhan; Han, Xudong; Guo, Weijie; Wan, Fang; Pan, Jia; Song, Chaoyang

doi:10.1109/lra.2021.3065186

Cited by 18 publications

(14 citation statements)

References 24 publications

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“…However, since the strain sensors are normally made for one axis detection, they are usually insufficient for detection and need to be used together with other sensors such as tactile ( Zuo et al, 2021 ) and vision ( Jiao et al, 2020 ) sensors. Tactile sensors are widely used for detecting objects ( Jiao et al, 2020 ; She et al, 2020 ; YangHan et al, 2020 ; Subad et al, 2021 ; Zuo et al, 2021 ; Deng et al, 2022 ). They can be built and fabricated on a small scale and embedded into soft grippers.…”

Section: Methods and Recent Developmentsmentioning

confidence: 99%

Learning-based robotic grasping: A review

2023

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As personalization technology increasingly orchestrates individualized shopping or marketing experiences in industries such as logistics, fast-moving consumer goods, and food delivery, these sectors require flexible solutions that can automate object grasping for unknown or unseen objects without much modification or downtime. Most solutions in the market are based on traditional object recognition and are, therefore, not suitable for grasping unknown objects with varying shapes and textures. Adequate learning policies enable robotic grasping to accommodate high-mix and low-volume manufacturing scenarios. In this paper, we review the recent development of learning-based robotic grasping techniques from a corpus of over 150 papers. In addition to addressing the current achievements from researchers all over the world, we also point out the gaps and challenges faced in AI-enabled grasping, which hinder robotization in the aforementioned industries. In addition to 3D object segmentation and learning-based grasping benchmarks, we have also performed a comprehensive market survey regarding tactile sensors and robot skin. Furthermore, we reviewed the latest literature on how sensor feedback can be trained by a learning model to provide valid inputs for grasping stability. Finally, learning-based soft gripping is evaluated as soft grippers can accommodate objects of various sizes and shapes and can even handle fragile objects. In general, robotic grasping can achieve higher flexibility and adaptability, when equipped with learning algorithms.

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Section: Methods and Recent Developmentsmentioning

confidence: 99%

Learning-based robotic grasping: A review

2023

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“…Using these kinds of tactile sensors are beneficial for achieving not only manipulation but also object recognition or detection of object-related events such as slip [18]. Tactile exploration is a good way to effectively acquire such object information [19] [20]. Many multi-fingered hands with such tactile sensors were developed [21] [22] but they focused on only fingertips.…”

Section: B Object Property and Processing Tactile Informationmentioning

confidence: 99%

Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors

Funabashi

Isobe

Hongyi

et al. 2022

IEEE Robot. Autom. Lett.

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Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural networks (CNN) can be useful for processing tactile information, but the information from multi-fingered hands needs an arbitrary pre-processing, as CNNs require a rectangularly shaped input, which may lead to unstable results. Therefore, how to process such complex shaped tactile information and utilize it for achieving manipulation skills is still an open issue. This paper presents a control method based on a graph convolutional network (GCN) which extracts geodesical features from the tactile data with complicated sensor alignments. Moreover, object property labels are provided to the GCN to adjust in-hand manipulation motions. Distributed tri-axial tactile sensors are mounted on the fingertips, finger phalanges and palm of an Allegro hand, resulting in 1152 tactile measurements. Training data is collected with a data-glove to transfer human dexterous manipulation directly to the robot hand. The GCN achieved high success rates for in-hand manipulation. We also confirmed that fragile objects were deformed less when correct object labels were provided to the GCN. When visualizing the activation of the GCN with a PCA, we verified that the network acquired geodesical features. Our method achieved stable manipulation even when an experimenter pulled a grasped object and for untrained objects. A Project page including accompanying video and supplementary materials can be found at https://sites.google.com/site/bashifunabashi/multifinger-project/in-hand-manipulation

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“…In the future, we hope to use computational method to analyze the arrangements of the teeth profiles on the lobster claws and generate a series of designs based on the arrangements automatically to enlarge the design pool. • In our previous research, we have successfully added optical fibers to the finger and used the data collected from the fibers to predict force, torque, and contact on the finger based on machine learning methods ( Yang et al, 2021 ). In the future, we will install these fibers into the finger surface so that the surface itself can “feel” the object.…”

Section: Final Remarksmentioning

confidence: 99%

“…• In our previous research, we have successfully added optical fibers to the finger and used the data collected from the fibers to predict force, torque, and contact on the finger based on machine learning methods ( Yang et al, 2021 ). In the future, we will install these fibers into the finger surface so that the surface itself can “feel” the object.…”

Section: Final Remarksmentioning

confidence: 99%

Rigid–Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater

et al. 2021

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Bio-inspirations from soft-bodied animals provide a rich design source for soft robots, yet limited literature explored the potential enhancement from rigid-bodied ones. This paper draws inspiration from the tooth profiles of the rigid claws of the Boston Lobster, aiming at an enhanced soft finger surface for underwater grasping using an iterative design process. The lobsters distinguish themselves from other marine animals with a pair of claws capable of dexterous object manipulation both on land and underwater. We proposed a 3-stage design iteration process that involves raw imitation, design parametric exploration, and bionic parametric exploitation on the original tooth profiles on the claws of the Boston Lobster. Eventually, 7 finger surface designs were generated and fabricated with soft silicone. We validated each design stage through many vision-based robotic grasping attempts against selected objects from the Evolved Grasping Analysis Dataset (EGAD). Over 14,000 grasp attempts were accumulated on land (71.4%) and underwater (28.6%), where we selected the optimal design through an on-land experiment and further tested its capability underwater. As a result, we observed an 18.2% improvement in grasping success rate at most from a resultant bionic finger surface design, compared with those without the surface, and a 10.4% improvement at most compared with the validation design from the previous literature. Results from this paper are relevant and consistent with the bioresearch earlier in 1911, showing the value of bionics. The results indicate the capability and competence of the optimal bionic finger surface design in an amphibious environment, which can contribute to future research in enhanced underwater grasping using soft robots.

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Learning-Based Optoelectronically Innervated Tactile Finger for Rigid-Soft Interactive Grasping

Cited by 18 publications

References 24 publications

Learning-based robotic grasping: A review

Learning-based robotic grasping: A review

Multi-Fingered In-Hand Manipulation With Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors

Rigid–Soft Interactive Design of a Lobster-Inspired Finger Surface for Enhanced Grasping Underwater

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