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The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
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I. Rodrıguez, K. Nottensteiner, D. Leidner, M. Kaßecker, F. Stulp, and A. Albu-Schäffer, “Iteratively refined feasibility checks in robotic assembly sequence planning,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1416–1423, 2019
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K. D. Willis, P. K. Jayaraman, H. Chu, Y. Tian, Y. Li, D. Grandi, A. Sanghi, L. Tran, J. G. Lambourne, A. Solar-Lezama, and W. Matusik, “Joinable: Learning bottom-up assembly of parametric cad joints,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022
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N. Funk, S. Menzenbach, G. Chalvatzaki, and J. Peters, “Graph-based reinforcement learning meets mixed integer programs: An application to 3d robot assembly discovery,” 2022
2022
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K. Kitz and U. Thomas, “Neural dynamic assembly sequence planning,” in 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) . IEEE, 2021, pp. 2063–2068
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