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We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations.
J. Carvalho, A. T. Le, M. Baierl, D. Koert, and J. Peters, “Motion planning diffusion: Learning and planning of robot motions with diffusion models,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 1916–1923
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L. Dorst, D. Fontijne, and S. Mann, Geometric Algebra for Computer Science: An Object-Oriented Approach to Geometry , 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2007
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C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen, “The columbia grasp database,” in 2009 IEEE international conference on robotics and automation . IEEE, 2009, pp. 1710–1716
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J. Varley, J. Weisz, J. Weiss, and P. Allen, “Generating multi-fingered robotic grasps via deep learning,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2015, pp. 4415–4420
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
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P. Schmidt, N. Vahrenkamp, M. Wächter, and T. Asfour, “Grasping of unknown objects using deep convolutional neural networks based on depth images,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 6831–6838
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C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis, “Learning so (3) equivariant representations with spherical cnns,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 52–68
2018
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R. Kondor and S. Trivedi, “On the generalization of equivariance and convolution in neural networks to the action of compact groups,” in International conference on machine learning . PMLR, 2018, pp. 2747–2755
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J. Liang, V. Makoviychuk, A. Handa, N. Chentanez, M. Macklin, and D. Fox, “Gpu-accelerated robotic simulation for distributed reinforcement learning,” in Conference on Robot Learning . PMLR, 2018, pp. 270–282
2018
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S. Brahmbhatt, A. Handa, J. Hays, and D. Fox, “Contactgrasp: Functional multi-finger grasp synthesis from contact,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 2386–2393
2019
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A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 2901–2910
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T. S. Cohen, M. Geiger, and M. Weiler, “A general theory of equivariant cnns on homogeneous spaces,” Advances in neural information processing systems , vol. 32, 2019
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Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li, “On the continuity of rotation representations in neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 5745–5753
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E. Corona, A. Pumarola, G. Alenya, F. Moreno-Noguer, and G. Rogez, “Ganhand: Predicting human grasp affordances in multi-object scenes,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 5031–5041
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M. Liu, Z. Pan, K. Xu, K. Ganguly, and D. Manocha, “Deep differentiable grasp planner for high-dof grippers,” in Robotics: Science and Systems , 2020
2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
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J. Köhler, L. Klein, and F. Noé, “Equivariant flows: exact likelihood generative learning for symmetric densities,” in International conference on machine learning . PMLR, 2020, pp. 5361–5370
2020
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J. Lundell, F. Verdoja, and V. Kyrki, “Ddgc: Generative deep dexterous grasping in clutter,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 6899–6906, 2021
2021
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P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in neural information processing systems , vol. 34, pp. 8780–8794, 2021
M. Macklin, “Warp: A high-performance python framework for gpu simulation and graphics,” https://github.com/nvidia/warp , March 2022, nVIDIA GPU Technology Conference (GTC)
2022
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D. Turpin, T. Zhong, S. Zhang, G. Zhu, E. Heiden, M. Macklin, S. Tsogkas, S. Dickinson, and A. Garg, “Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8082–8089
2023
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P. Li, T. Liu, Y. Li, Y. Geng, Y. Zhu, Y. Yang, and S. Huang, “Gendexgrasp: Generalizable dexterous grasping,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8068–8074
2023
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S. Huang, Z. Wang, P. Li, B. Jia, T. Liu, Y. Zhu, W. Liang, and S.-C. Zhu, “Diffusion-based generation, optimization, and planning in 3d scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 16 750–16 761
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2021
Cited alongside, same era.
J. Lundell, E. Corona, T. N. Le, F. Verdoja, P. Weinzaepfel, G. Rogez, F. Moreno-Noguer, and V. Kyrki, “Multi-fingan: Generative coarse-to-fine sampling of multi-finger grasps,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 4495–4501
2021
Cited alongside, same era.
S. Zhu, B. An, and F. Huang, “Understanding the generalization benefit of model invariance from a data perspective,” Advances in Neural Information Processing Systems , vol. 34, pp. 4328–4341, 2021
2021
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C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas, “Vector neurons: A general framework for so (3)-equivariant networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 200–12 209
2021
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T. Liu, Z. Liu, Z. Jiao, Y. Zhu, and S.-C. Zhu, “Synthesizing diverse and physically stable grasps with arbitrary hand structures using differentiable force closure estimator,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 470–477, 2021
2021
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2021
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G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” Journal of Machine Learning Research , vol. 22, no. 57, pp. 1–64, 2021
2021
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H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 16 259–16 268
2021
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2023
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D. Ruhe, J. K. Gupta, S. De Keninck, M. Welling, and J. Brandstetter, “Geometric clifford algebra networks,” in International Conference on Machine Learning . PMLR, 2023, pp. 29 306–29 337
2023
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D. Ruhe, J. Brandstetter, and P. Forré, “Clifford group equivariant neural networks,” Advances in Neural Information Processing Systems , vol. 36, 2023
2023
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J. Brehmer, P. De Haan, S. Behrends, and T. S. Cohen, “Geometric algebra transformer,” Advances in Neural Information Processing Systems , vol. 36, 2023
2023
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2023
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J. Urain, N. Funk, J. Peters, and 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
2023
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X. Fang, C. Garrett, C. Eppner, T. Lozano-Pérez, L. Kaelbling, and D. Fox, “Dimsam: Diffusion models as samplers for task and motion planning under partial observability,” in CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP) , 2023
2023
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A. Sridhar, D. Shah, C. Glossop, and S. Levine, “Nomad: Goal masked diffusion policies for navigation and exploration,” in First Workshop on Out-of-Distribution Generalization in Robotics at CoRL 2023 , 2023
2023
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C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song, “Diffusion policy: Visuomotor policy learning via action diffusion,” in Proceedings of Robotics: Science and Systems (RSS) , 2023
2023
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Z. Xian, N. Gkanatsios, T. Gervet, and K. Fragkiadaki, “Unifying diffusion models with action detection transformers for multi-task robotic manipulation,” in 7th Annual Conference on Robot Learning , 2023
2023
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U. Mishra and Y. Chen, “Reorientdiff: Diffusion model based reorientation for object manipulation,” in CoRL 2023 Workshop on Learning Effective Abstractions for Planning (LEAP) , 2023
2023
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J. Lei, C. Deng, K. Schmeckpeper, L. Guibas, and K. Daniilidis, “Efem: Equivariant neural field expectation maximization for 3d object segmentation without scene supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4902–4912
2023
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2024
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H. Ryu, J. Kim, J. Chang, H. S. Ahn, J. Seo, T. Kim, J. Choi, and R. Horowitz, “Diffusion-edfs: Bi-equivariant denoising generative modeling on se (3) for visual robotic manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
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J. Brehmer, J. Bose, P. De Haan, and T. S. Cohen, “Edgi: Equivariant diffusion for planning with embodied agents,” Advances in Neural Information Processing Systems , vol. 36, 2024
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