A Robotic Grasping Method using ConvNets

Ogas, Elio; Ávila, Luis; Larregay, Guillermo; Moran, Daniel Humberto Plua

doi:10.1109/cae.2019.8709277

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…The model was tested on Cornell Grasp Dataset. A robotic grasping method that consists of a ConvNet is discussed by Ogas et al [31] for object recognition and a grasping method for manipulating the objects. The grasping method assumes an industry assembly line where the object parameters are assumed to be known in advance.…”

Section: Related Workmentioning

confidence: 99%

Robotic grasp detection using deep convolutional neural networks

Kumra

Kanan

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

413

320

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Abstract-Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this paper, we present a novel robotic grasp detection system that predicts the best grasping pose of a parallel-plate robotic gripper for novel objects using the RGB-D image of the scene. The proposed model uses a deep convolutional neural network to extract features from the scene and then uses a shallow convolutional neural network to predict the grasp configuration for the object of interest. Our multi-modal model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and runs at real-time speeds. This redefines the state-of-the-art for robotic grasp detection.

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Section: Related Workmentioning

confidence: 99%

Robotic grasp detection using deep convolutional neural networks

Kumra

Kanan

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

413

320

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“…Chu et al [12] proposed a novel architecture capable of simultaneously predicting multiple grasps for multiple objects. Ogas et al [13] discussed a robotic grasping method combining ConvNet for object recognition and a grasping method for objects with known parameters. Kumra et al [14] introduced a Deep CNN architecture using residual layers for predicting robust grasps, highlighting the advantages of a deeper network with residual layers.…”

Section: Related Workmentioning

confidence: 99%

Safety-optimized Strategy for Grasp Detection in High-clutter Scenarios

Li,

Zhou,

Chong

2024

Preprint

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The detection accuracy and speed of grasp detection models on benchmarks are the focal points of concern in the robotic grasping community. Especially in a collaborative robot setting, the safety of the model is an essential aspect that cannot be overlooked. In this paper, we explore how to enhance the safety of grasp detection models in autonomous vision-guided grasping. Specifically, we propose a simple yet practical Safety-optimized Strategy, which consists of two parts. The first part involves depth prioritization, optimizing the grasp sequence from top to bottom based on the order of depth values, which can mitigate the issue of grasp collisions that may arise when the depth value of the object with the highest grasp quality is significantly higher than that of other objects in high-clutter scenarios. The second part is false-positive protection, where we introduce the robust Aruco marker as the lowest grasp priority. The marker is fixed at certain positions within the camera’s field of view, enabling the robot to halt its movement, thereby restraining the robot from grasping objects that should not be grasped. Once the marker disappears, the robot can resume its operations. We validate our method through real grasping experiments with a parallel-jaw gripper and an industrial robotic arm, demonstrating its effectiveness in high-clutter scenarios.

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“…In recent years, many people have tried to apply this technology to grasp detection. Elio Ogas et al used Convolutional Neural Networks for recognizing a selected production piece on a cluster [15] , and it achieved a good performance using a common webcam as image input. In the same year, Lu et al proposed a novel grasp detection model that was constructed to make a fairer evaluation on grasp candidate using the grasp path [5] .…”

Section: Related Workmentioning

confidence: 99%

Model Adaption Object Detection System for Robot

Zong

et al. 2020

2020 39th Chinese Control Conference (CCC)

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Object detection for robot guidance is a crucial mission for autonomous robots, which has provoked extensive attention for researchers. However, the changing view of robot movement and limited available data hinder the research in this area. To address these matters, we proposed a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, We made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks. Furthermore, taking advantage of transfer learning technology and depthwise separable convolutions, our model is easy to train and can address small dataset problems.

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A Robotic Grasping Method using ConvNets

Cited by 10 publications

References 20 publications

Robotic grasp detection using deep convolutional neural networks

Robotic grasp detection using deep convolutional neural networks

Safety-optimized Strategy for Grasp Detection in High-clutter Scenarios

Model Adaption Object Detection System for Robot

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