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Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at each layer in the hierarchy.
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2021
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2021
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S. James, K. Wada, T. Laidlow, and A. J. Davison, “Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation,” IEEE Conference on Computer Vision and Pattern Recognition , 2022
2022
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S. James and A. J. Davison, “Q-attention: Enabling efficient learning for vision-based robotic manipulation,” IEEE Robotics and Automation Letters , 2022
2022
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2020
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K. Wada, E. Sucar, S. James, D. Lenton, and A. J. Davison, “MoreFusion: Multi-object reasoning for 6D pose estimation from volumetric fusion,” in IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 540–14 549
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K. Wada, S. James, and A. J. Davison, “ReorientBot: Learning object reorientation for specific-posed placement,” IEEE Intl. Conference on Robotics and Automation , 2022
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