Benchmarking the Grasping Capabilities of the iCub Hand With the YCB Object and Model Set

Jamone, Lorenzo; Bernardino, Alexandre; Santos-Victor, José

doi:10.1109/lra.2016.2517209

Cited by 11 publications

(7 citation statements)

References 19 publications

(21 reference statements)

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“…In addition, it also includes the research history and research results of computer simulation painting technology and multitouch technology. 21 In the research based on rigid palm dexterous hand grasping, scholars generally focus on how to use arm motion planning to move the end effector, namely the rigid palm, to a specified position, and finally complete the grasping action through finger movements. For traditional mechanical dexterous hands, its flexibility is reflected in how to improve the layout of the fingers and increase the extra freedom of the fingers.…”

Section: Discussionmentioning

confidence: 99%

(Retracted) Virtual reality painting dexterous hand gesture control algorithm and simulation

Xue

Yang

2022

J. Electron. Imag.

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. With the expansion of the application field of robots and the complexity of the work environment and tasks, ordinary robots and various simple end-holding devices that cooperate with them are far from being able to meet the requirements of various dexterity and fine operation tasks. Immersion means that the process of painting is virtual immersion; the expression requirement for painting must be immersive. This research mainly discusses the posture control algorithm and simulation of dexterous hand in virtual reality painting. The research combines the related theoretical basis of the dexterous hand posture control algorithm with VR painting, and explores the immersive experience characteristics brought by VR immersive painting. This research will focus on the three aspects of the action state pattern recognition based on the surface electromyogram (EMG) signal, the algorithm of the dexterous hand virtual control system, and the simulation of the bionic dexterous hand EMG control system. The collection and processing of the surface EMG signal of the forearm were completed. The experiment verified the effectiveness and accuracy of the surface EMG signal feature extraction, feature dimensionality reduction, and classification algorithm, and realized the pattern recognition of eight action states. Using musculoskeletal simulation software and a finite state machine control model, an intelligent bionic dexterous hand virtual control system was established, which realized offline gesture recognition and performance evaluation of the virtual control system. In the environment of large time delay, many painting experiments were carried out, the tasks were successfully completed, and the average completion time was about 4 min. At this stage, most of the finger structures of dexterous hands are in series, and the accumulated error is large, which greatly affects the accuracy of dexterous hands. VR painting is an innovative form of painting with potential space in the future. The results of this research and exploration can reflect the potential possibilities of future painting creation forms.

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Section: Discussionmentioning

confidence: 99%

(Retracted) Virtual reality painting dexterous hand gesture control algorithm and simulation

Xue

Yang

2022

J. Electron. Imag.

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“…With reference to the adaptive grasping action of the human hand, the multi-joint pneumatic hand is provided with an appropriate amount of air pressure for the grasping behaviour of the fingers to undergo active adaptive deformation. The YCB (Yale -CMU -Berkeley) set of grasped items [5] is selected as the object to be grasped for the experiment, and can be divided into four main categories: food, kitchenware, tools and shapes. Figure 5 illustrates the grasping performance of a representative number of objects.…”

Section: Grasping Performance Testsmentioning

confidence: 99%

“…In recent years, rigid manipulators have achieved good functionality by introducing many elements such as motors, linkages, gears and springs [2][3][4], but at the same time greatly increasing the sophistication of structures. Although the rigid robotic hands are precise and responsive, they may cause irreparable damage when gripping fragile things [5]. Due to the softness of its material, the soft hand has a unique advantage in terms of compliance and toughness, not only it can grasp different kinds of objects, but also it will hardly be damaged when it is extruded and collided with by outside [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Flexible soft Pneumatic Bionic Hand Based on Multi-Jointed Structure

Hao

Xiao

Liu

2023

J. Phys.: Conf. Ser.

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Compared with rigid robotic hands, soft hands can provide better safety and adaptability. In the process of gradual development, the multi-jointed structure that mimics the shape of the human hand has shown significant progress in realizing its personification. In this article, we propose a multi-jointed pneumatic soft hand, which is composed of multi-jointed soft fingers, thumb, thenar and 3D printed palm. It can express letters through sign language gestures and can grasp objects with different sizes, shapes, weights and surface textures. We tested the bending ability of the actuators under different air pressures to characterize the performance of actuators made of silicone rubber. Based on the strain energy density function of silicone rubber, the Yeoh model is used to calculate the relationship between the air pressure required and the bending angle. In addition, a dedicated pneumatic control system is designed and manufactured to enable the soft hand to automatically perform tasks set by the specific program. This new multi-jointed pneumatic soft hand has flexible bionic fingers, and has the advantages of fast response speed, low cost, easy manufacturing, assembly and replacement.

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“…Grasping Benchmark 4: extended GAB. An extension to the standard GAB was proposed by Jamone et al 47 to include three more categories of objects: Cubic, Cylindrical, and Complex. The addition of further objects provides more information about the types of object graspable by the T-MO.…”

Section: Grasping Benchmarksmentioning

confidence: 99%

“…Extended Scoring of the Tactile Model O Hand in the Basic Gripper AssessmentBenchmark as Proposed by Jamone et al47 …”

mentioning

confidence: 99%

Tactile Model O: Fabrication and Testing of a 3D-Printed, Three-Fingered Tactile Robot Hand

et al. 2021

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Benchmarking the Grasping Capabilities of the iCub Hand With the YCB Object and Model Set

Cited by 11 publications

References 19 publications

(Retracted) Virtual reality painting dexterous hand gesture control algorithm and simulation

(Retracted) Virtual reality painting dexterous hand gesture control algorithm and simulation

Flexible soft Pneumatic Bionic Hand Based on Multi-Jointed Structure

Tactile Model O: Fabrication and Testing of a 3D-Printed, Three-Fingered Tactile Robot Hand

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