Real-Time Grasp Detection Using Efficient Channel Attention
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9243-0_10
Reference17 articles.
1. Rubert, C., Kappler, D., Morales A., et al.: On the relevance of grasp metrics for predicting grasp success. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 265–272 (2017)
2. Cheng, H., Meng, M.Q.H.: A grasp pose detection scheme with an end-to-end CNN regression approach. In: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 544–549 (2018)
3. Chu, F.J., Xu, R., Vela, P.A.: Real-world multiobject, multigrasp detection. IEEE Robot. Autom. Lett. 3(4), 3355–3362 (2018)
4. Cheng, H., Wang, Y., Meng, M.Q.H.: A vision-based robot grasping system. IEEE Sens. J. 22(10), 9610–9620 (2022)
5. Song, Y., Gao, L., Li, X., et al.: A novel robotic grasp detection method based on region proposal networks. Robot. Comput.-Integr. Manuf. 65, 101963 (2020)
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