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Articulated object manipulation requires precise object interaction, where the object's axis must be carefully considered.
1910
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K. Hausman, S. Niekum, S. Osentoski, and G. S. Sukhatme, “Active articulation model estimation through interactive perception,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 3305–3312
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Y. Karayiannidis, C. Smith, F. E. V. Barrientos, P. Ögren, and D. Kragic, “An adaptive control approach for opening doors and drawers under uncertainties,” IEEE Transactions on Robotics , vol. 32, no. 1, pp. 161–175, 2016
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J. Bohg, K. Hausman, B. Sankaran, O. Brock, D. Kragic, S. Schaal, and G. S. Sukhatme, “Interactive perception: Leveraging action in perception and perception in action,” IEEE Transactions on Robotics , vol. 33, no. 6, p. 1273–1291, Dec. 2017. [Online]. Available: http://dx.doi.org/10.1109/TRO.2017.2721939
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J. Wang, S. Lin, C. Hu, Y. Zhu, and L. Zhu, “Learning semantic keypoint representations for door opening manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6980–6987, 2020
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M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 459–13 466
2021
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W. Gao and R. Tedrake, “kpam-sc: Generalizable manipulation planning using keypoint affordance and shape completion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6527–6533
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2021
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Z. Jiang, C.-C. Hsu, and Y. Zhu, “Ditto: Building digital twins of articulated objects from interaction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5616–5626
2022
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R. Wu, Y. Zhao, K. Mo, Z. Guo, Y. Wang, T. Wu, Q. Fan, X. Chen, L. Guibas, and H. Dong, “VAT-mart: Learning visual action trajectory proposals for manipulating 3d ARTiculated objects,” in International Conference on Learning Representations , 2022
2022
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Z. Xu, Z. He, and S. Song, “Universal manipulation policy network for articulated objects,” IEEE Robotics and Automation Letters , vol. 7, no. 2, p. 2447–2454, Apr. 2022. [Online]. Available: http://dx.doi.org/10.1109/LRA.2022.3142397
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2023
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S. Afrin, S. Roksana, and R. Akram, “Ai-enhanced robotic process automation: A review of intelligent automation innovations,” IEEE Access , 2024
2024
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P. Picozzi, U. Nocco, C. Labate, I. Gambini, G. Puleo, F. Silvi, A. Pezzillo, R. Mantione, and V. Cimolin, “Advances in robotic surgery: A review of new surgical platforms,” Electronics , vol. 13, no. 23, p. 4675, 2024
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B. An, Y. Geng, K. Chen, X. Li, Q. Dou, and H. Dong, “Rgbmanip: Monocular image-based robotic manipulation through active object pose estimation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 7748–7755
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