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In many complex scenarios, robotic manipulation relies on generative models to estimate the distribution of multiple successful actions.
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J. Urain, N. Funk, J. Peters, G. Chalvatzaki, Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, in: 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2023, pp. 5923–5930
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H. Wu, Y. Jing, C. Cheang, G. Chen, J. Xu, X. Li, M. Liu, H. Li, T. Kong, Unleashing large-scale video generative pre-training for visual robot manipulation, in: The Twelfth International Conference on Learning Representations, 2024
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X. Ma, S. Patidar, I. Haughton, S. James, Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18081–18090
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Q. Zhang, Z. Liu, H. Fan, G. Liu, B. Zeng, S. Liu, Flowpolicy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, 2025, pp. 14754–14762
2025
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