Structured Local Feature-Conditioned 6-DOF Variational Grasp Detection Network in Cluttered Scenes

One of the most crucial abilities for robots is to grasp objects accurately in cluttered scenes. This article proposes a structured local feature-conditioned 6-DOF variational grasp detection network (LF-GraspNet) that can generate accurate grasp configurations in cluttered scenes end to end. First,...

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Vydané v:IEEE/ASME transactions on mechatronics s. 1 - 11
Hlavní autori: Liu, Hongyang, Li, Hui, Jiang, Changhua, Xue, Shuqi, Zhao, Yan, Huang, Xiao, Jiang, Zhihong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2024
Predmet:
6-DOF
Accuracy
Conditional variational autoencoder (CVAE)
convolutional neural network (CNN)
Convolutional neural networks
Feature extraction
grasp detection
Grasping
Mechatronics
Point cloud compression
Quaternions
robot grasping
Robots
Three-dimensional displays
ISSN:1083-4435, 1941-014X
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Shrnutí:One of the most crucial abilities for robots is to grasp objects accurately in cluttered scenes. This article proposes a structured local feature-conditioned 6-DOF variational grasp detection network (LF-GraspNet) that can generate accurate grasp configurations in cluttered scenes end to end. First, we propose a network using a 3-D convolutional neural network with a conditional variational autoencoder (CVAE) as a backbone. The explorability of the VAE enhances the network's generalizability in grasp detection. Second, we jointly encode the truncated signed distance function (TSDF) of the scene and successful grasp configurations into the global feature as the prior of the latent space of the CVAE. The structured local feature of the TSDF volume is used as the condition of the CVAE, which can then skillfully fuse different modalities and scales of features. Simulation and real-world grasp experiments demonstrate that LF-GraspNet, trained on a grasp dataset with a limited number of primitive objects, achieves better success rates and declutter rates for unseen objects in cluttered scenes than baseline methods. Specifically, in real-world grasp experiments, LF-GraspNet achieves stable grasping of objects in cluttered scenes with single-view and multiview depth images as input, demonstrating its excellent grasp performance and generalization ability from simple primitive objects to complex and unseen objects.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2024.3500577