結合改良式物件姿態估測之最佳機器人夾取策略

Optimal Robotic Grasping Strategy Incorporating Improved Object Pose Estimation

游鈞凱(You Jiun-Kai)

指導教授 ： 許陳鑑

國立臺灣師範大學/科技與工程學院/電機工程學系/碩士(2021年)

https://doi.org/10.6345/NTNU202100110

摘要

無資料

關鍵字

none

並列摘要

無資料

並列關鍵字

object pose estimation ； LINEMOD ； Occlusion LINEMOD ； deep learning ； convolutional neural network ； grasp strategy

參考文獻

[1] [Online]. Available https://ifr.org/

[2] [Online]. Available https://reurl.cc/j5b9aq

[3] D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine, “QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation,” Proceedings 10 of The 2nd Conference on Robot Learning, volume 87 of Proceedings of Machine Learning Research, PMLR, 2018, pp. 651–673

[4] A. Bicchi and V. Kumar, “Robotic grasping and contact: a review,” Proceedings 2000 ICRA. Millennium Conference, IEEE International Conference on Robotics and Automation. Symposia Proceedings, San Francisco, CA, USA, 2000, pp. 348-353 vol.1.

[5] J. Bohg, A. Morales, T. Asfour and D. Kragic, “Data-Driven Grasp Synthesis—A Survey,” IEEE Transactions on Robotics, April 2014, vol. 30, no. 2, pp. 289-309.