The impact of visual cues on haptic compliance discrimination using a pseudo-haptic robotic system

Fakhoury, Evan; Culmer, Peter; Henson, Brian

doi:10.1109/robio.2017.8324666

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…Recent advancements in robotics and autonomous systems have resulted in an increase in autonomous capabilities and the use of more intelligent machines in a wider range of applications [1][2][3][4]. Gripping various geometric objects is a key competency for robots to achieve more general-purpose functionality [5].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.

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Section: Introductionmentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Pseudo-Haptics Survey: Human-Computer Interaction in Extended Reality and Teleoperation

Xavier,

Silva,

Ventura

et al. 2024

IEEE Access

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Quality, Quantity and Impact in Engineering Research: Case Study of a Non-Doctoral University in the Arab World

El-Zahr

2022

2022 IEEE Integrated STEM Education Conference (ISEC)

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The impact of visual cues on haptic compliance discrimination using a pseudo-haptic robotic system

Abstract: This is a repository copy of The impact of visual cues on haptic compliance discrimination using a pseudo-haptic robotic system.

Cited by 6 publications

References 24 publications

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Pseudo-Haptics Survey: Human-Computer Interaction in Extended Reality and Teleoperation

Quality, Quantity and Impact in Engineering Research: Case Study of a Non-Doctoral University in the Arab World

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