Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://link.springer.com/content/pdf/10.1007/s00138-023-01459-2.pdf
Reference45 articles.
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2. Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis-a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)
3. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)
4. Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Ojea, J.A., Goldberg, K.: Dex-net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. CoRR arXiv:1703.09312 (2017)
5. Pinto, L., Gupta, A.: Supersizing self-supervision: learning to grasp from 50k tries and 700 robot hours. CoRR arXiv:1509.06825 (2015)
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1. LiteGrasp: A Light Robotic Grasp Detection via Semi-Supervised Knowledge Distillation;IEEE Robotics and Automation Letters;2024-09
2. Robotic Grasp Detection Using Structure Prior Attention and Multiscale Features;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024
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