Center-of-Mass-Based Robust Grasp Pose Adaptation Using RGBD Camera and Force/Torque Sensing
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-6613-2_263
Reference9 articles.
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2. Feng, Q., Chen, Z., Deng, J., Gao, C., Zhang, J., Knoll, A.C.: Center-of-mass-based robust grasp planning for unknown objects using tactile-visual sensors. CoRR abs/2006.00906 (2020). arXiv.org/abs/2006.00906
3. Jiang, Y., Moseson, S., Saxena, A.: Efficient grasping from RGBD images: learning using a new rectangle representation. In: 2011 IEEE International Conference on Robotics and Automation, pp. 3304–3311 (2011). https://doi.org/10.1109/ICRA.2011.5980145
4. Kanoulas, D., Lee, J., Caldwell, D.G., Tsagarakis, N.G.: Center-of-mass-based grasp pose adaptation using 3D range and force/torque sensing. CoRR abs/1802.06392 (2018). arXiv.org/abs/1802.06392
5. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. In: CVPR (2014). arXiv.org/abs/1301.3592
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