Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

Kumra, Sulabh; Joshi, Shirin; Sahin, Ferat

doi:10.48550/arxiv.1909.04810

Cited by 2 publications

(2 citation statements)

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“…For example, Lenz et al created the Cornell Grasp Dataset consisting of 1,035 RGB-D images of 280 different objects with manual labels [25]. A recent research work extended the Cornell Grasp dataset to 51 K grasp examples and trained a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model [23]. Researchers also attempted to develop a model with another approach: Reinforcement Learning.…”

Section: Related Workmentioning

confidence: 99%

A CNN-Based Grasp Planning Method for Random Picking of Unknown Objects with a Vacuum Gripper

Zhang

Peeters²,

Demeester³

et al. 2021

J Intell Robot Syst

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Robotic grasping is still challenging due to limitations in perception and control, especially when the CAD models of objects are unknown. Although some grasp planning approaches using computer vision have been proposed, these methods can be seen as openloop grasp planning methods and are often not robust enough. In this paper, a novel grasp planning method combining CNN-based quality prediction and closed-loop control (CNNB-CL) is proposed for a vacuum gripper. A large-scale dataset is generated for CNN training, which contains more than 2.3 million synthetic grasps and their grasp qualities evaluated by grasp simulations with 3D models. Unlike other neural networks which predict grasp success by assigning a binary value or grasp quality level by assigning an integer value, the proposed CNN predicts the grasp quality via a linear regression architecture. Additionally, the method adjusts the grasp strategies and detects the optimal grasp based on feedback from a force-torque sensor. Various simulations and physical experiments prove that the CNNB-CL method is robust for random noise disturbance in observation and compatible with different depth cameras and vacuum grippers. The proposed method finds the optimal grasp from 2,000 candidates within 300 ms and achieves a 92.18% average success rate for different vacuum grippers, which outperforms the state-of-the-art methods regarding success rate and robustness.

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Section: Related Workmentioning

confidence: 99%

A CNN-Based Grasp Planning Method for Random Picking of Unknown Objects with a Vacuum Gripper

Zhang

Peeters²,

Demeester³

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…Two different types of VAEs were explored in this work, conditional variational autoencoders (CVAE) [66] and vector quantized variational autoencoders (VQ-VAE) [67]. Similar to other grasp map estimation models such as [64,68], these models are also very lightweight and are able to generate grasp poses with relatively high speed with a response time of around 19 ms. Evaluation of these approaches on the Cornell dataset also demonstrated a high grasp detection accuracy of 95.4% for the VQ-VAE and 94.3% for the CVAE-based models.…”

Section: Grasp Convolutional Neural Network With Variational Autoenco...mentioning

confidence: 99%

Robot-Assisted Glovebox Teleoperation for Nuclear Industry

et al. 2021

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The nuclear industry has some of the most extreme environments in the world, with radiation levels and extremely harsh conditions restraining human access to many facilities. One method for enabling minimal human exposure to hazards under these conditions is through the use of gloveboxes that are sealed volumes with controlled access for performing handling. While gloveboxes allow operators to perform complex handling tasks, they put operators at considerable risk from breaking the confinement and, historically, serious examples including punctured gloves leading to lifetime doses have occurred. To date, robotic systems have had relatively little impact on the industry, even though it is clear that they offer major opportunities for improving productivity and significantly reducing risks to human health. This work presents the challenges of robotic and AI solutions for nuclear gloveboxes, and introduces a step forward for bringing cutting-edge technology to gloveboxes. The problem statement and challenges are highlighted and then an integrated demonstrator is proposed for robotic handling in nuclear gloveboxes for nuclear material handling. The proposed approach spans from tele-manipulation to shared autonomy, computer vision solutions for robotic manipulation to machine learning solutions for condition monitoring.

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Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

Cited by 2 publications

References 0 publications

A CNN-Based Grasp Planning Method for Random Picking of Unknown Objects with a Vacuum Gripper

A CNN-Based Grasp Planning Method for Random Picking of Unknown Objects with a Vacuum Gripper

Robot-Assisted Glovebox Teleoperation for Nuclear Industry

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