Batch Normalization Masked Sparse Autoencoder for Robotic Grasping Detection

To improve the accuracy of the grasping detection, this paper proposes a novel detector with batch normalization masked evaluation model. It is designed with a two-layer sparse autoencoder, and a Batch Normalization based mask is incorporated into the second layer of the model to effectively reduce the features with weak correlation. The extracted features from such model are more distinctive, which guarantees the higher accuracy of the grasping detection. Extensive experiments show that the proposed evaluation model outperforms the state-of- the-art, and the recognition accuracy can reach 95. 51% for robotic grasping detection.

Authors

• Zhenzhou Shao
• Ying Qu 0001
• Guangli Ren
• Guohui Wang
• Yong Guan
• Zhiping Shi 0002
• Jindong Tan

Keywords

• Training
• Redundancy
• Grasping
• Detectors
• Feature extraction
• Intelligent robots
• Load modeling
• Batch Normalization
• Sparse Autoencoder
• Robotic Grasping
• Model Evaluation
• Deep Learning
• Overfitting
• Supervised Learning
• Hidden Layer
• Feature Learning
• Training Procedure
• Color Images
• Neurons In Layer
• Network Design
• Sparse Feature
• Unsupervised Way
• Surface Normal Vector