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Real2Sim2Real plays a critical role in robotic arm control and reinforcement learning, yet bridging this gap remains a significant challenge due to the complex physical properties of robots and the objects they manipulate.
S. Kucuk and Z. Bingul, “The inverse kinematics solutions of industrial robot manipulators,” in Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM ’04
2004
Earlier work this paper cites.
P. I. Corke, “A simple and systematic approach to assigning denavit–hartenberg parameters,” IEEE transactions on robotics
2007
Earlier work this paper cites.
E. Sifakis and J. Barbic, “Fem simulation of 3d deformable solids: a practitioner’s guide to theory, discretization and model reduction,” in Acm siggraph 2012 courses
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
U. Robots, “Collaborative robotic automation— cobots from universal robots,” 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
C. K. Liu and D. Negrut, “The role of physics-based simulators in robotics,” Annual Review of Control, Robotics, and Autonomous Systems
2021
Earlier work this paper cites.
M. Ding, Z. Chen, T. Du, P. Luo, J. Tenenbaum, and C. Gan, “Dynamic visual reasoning by learning differentiable physics models from video and language,” Advances In Neural Information Processing Systems
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y.-L. Qiao, J. Liang, V. Koltun, and M. C. Lin, “Efficient differentiable simulation of articulated bodies,” in International Conference on Machine Learning
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
J. Choe, S. Im, F. Rameau, M. Kang, and I. S. Kweon, “Volumefusion: Deep depth fusion for 3d scene reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
Earlier work this paper cites.
B. Chen, R. Kwiatkowski, C. Vondrick, and H. Lipson, “Fully body visual self-modeling of robot morphologies,” Science Robotics
2022
Earlier work this paper cites.
V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg, “Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,” in 2022 International Conference on Robotics and Automation (ICRA)
2022
Earlier work this paper cites.
Y. Ze, G. Yan, Y.-H. Wu, A. Macaluso, Y. Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang, “Multi-task real robot learning with generalizable neural feature fields,” CoRL
2023
Earlier work this paper cites.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics
2023
Earlier work this paper cites.
C. Jambon, B. Kerbl, G. Kopanas, S. Diolatzis, T. Leimkühler, and G. Drettakis, “Nerfshop: Interactive editing of neural radiance fields,” Proceedings of the ACM on Computer Graphics and Interactive Techniques
2023
Earlier work this paper cites.
D. Shim, S. Lee, and H. J. Kim, “Snerl: Semantic-aware neural radiance fields for reinforcement learning,” in International Conference on Machine Learning
2023
Earlier work this paper cites.
M. Tancik, E. Weber, E. Ng, R. Li, B. Yi, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, A. Ahuja, et al
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Z. Zhan, X. Li, Q. Li, H. He, A. Pandey, H. Xiao, Y. Xu, X. Chen, K. Xu, K. Cao, et al
2023
Cited alongside, same era.
Z. Wu, T. Liu, L. Luo, Z. Zhong, J. Chen, H. Xiao, C. Hou, H. Lou, Y. Chen, R. Yang, Y. Huang, X. Ye, Z. Yan, Y. Shi, Y. Liao, and H. Zhao, “Mars: An instance-aware, modular and realistic simulator for autonomous driving,” CICAI
2023
Cited alongside, same era.
Q. Dai, Y. Zhu, Y. Geng, C. Ruan, J. Zhang, and H. Wang, “Graspnerf: Multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf,” in 2023 IEEE International Conference on Robotics and Automation (ICRA)
2023
Cited alongside, same era.
I. Kapelyukh, V. Vosylius, and E. Johns, “Dall-e-bot: Introducing web-scale diffusion models to robotics,” IEEE Robotics and Automation Letters
2023
Cited alongside, same era.
2024
Closest in time.
B. Huang, Z. Yu, A. Chen, A. Geiger, and S. Gao, “2d gaussian splatting for geometrically accurate radiance fields,” in ACM SIGGRAPH 2024 Conference Papers
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Jiang, C. Wang, R. Zhang, J. Wu, and L. Fei-Fei, “Transic: Sim-to-real policy transfer by learning from online correction,” in Conference on Robot Learning
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J. Whitman, M. Travers, and H. Choset, “Learning modular robot control policies,” IEEE Transactions on Robotics
2023
Cited alongside, same era.
K. Wang, W. R. Johnson, S. Lu, X. Huang, J. Booth, R. Kramer-Bottiglio, M. Aanjaneya, and K. Bekris, “Real2sim2real transfer for control of cable-driven robots via a differentiable physics engine,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Q. Vuong, S. Levine, H. R. Walke, K. Pertsch, A. Singh, R. Doshi, C. Xu, J. Luo, L. Tan, D. Shah, et al
2023
Cited alongside, same era.
Q. Wang, Y.-Y. Chang, R. Cai, Z. Li, B. Hariharan, A. Holynski, and N. Snavely, “Tracking everything everywhere all at once,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2023
Cited alongside, same era.
S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa, “K-planes: Explicit radiance fields in space, time, and appearance,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Cited alongside, same era.
2024
Closest in time.
Y. Zheng, X. Chen, Y. Zheng, S. Gu, R. Yang, B. Jin, P. Li, C. Zhong, Z. Wang, L. Liu, et al
2024
Closest in time.
M. Bauza, A. Bronars, Y. Hou, I. Taylor, N. Chavan-Dafle, and A. Rodriguez, “Simple, a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects,” Science Robotics
2024
Closest in time.
2024
Closest in time.
J. Abou-Chakra, K. Rana, F. Dayoub, and N. Sünderhauf, “Physically embodied gaussian splatting: A realtime correctable world model for robotics,” in 8th Annual Conference on Robot Learning
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Weng, B. Wen, J. Tremblay, V. Blukis, D. Fox, L. Guibas, and S. Birchfield, “Neural implicit representation for building digital twins of unknown articulated objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
2024
Closest in time.
K. Ehsani, T. Gupta, R. Hendrix, J. Salvador, L. Weihs, K.-H. Zeng, K. P. Singh, Y. Kim, W. Han, A. Herrasti, et al
2024
Closest in time.
Y. Jiang, C. Yu, T. Xie, X. Li, Y. Feng, H. Wang, M. Li, H. Lau, F. Gao, Y. Yang, et al
2024
Closest in time.
T. Xie, Z. Zong, Y. Qiu, X. Li, Y. Feng, Y. Yang, and C. Jiang, “Physgaussian: Physics-integrated 3d gaussians for generative dynamics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
C. M. Kim, M. Wu, J. Kerr, K. Goldberg, M. Tancik, and A. Kanazawa, “Garfield: Group anything with radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
V. Modi, N. Sharp, O. Perel, S. Sueda, and D. I. Levin, “Simplicits: Mesh-free, geometry-agnostic elastic simulation,” ACM Transactions on Graphics (TOG)
2024
Closest in time.
Z. Yang, X. Gao, W. Zhou, S. Jiao, Y. Zhang, and X. Jin, “Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
2024
Closest in time.
H. Liu, C. Ye, Y. Nie, Y. He, and X. Han, “Lasa: Instance reconstruction from real scans using a large-scale aligned shape annotation dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
Y. Xiao, Q. Wang, S. Zhang, N. Xue, S. Peng, Y. Shen, and X. Zhou, “Spatialtracker: Tracking any 2d pixels in 3d space,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.
F. Barthel, A. Beckmann, W. Morgenstern, A. Hilsmann, and P. Eisert, “Gaussian splatting decoder for 3d-aware generative adversarial networks,” 2024
2024
Closest in time.
B. Wen, W. Yang, J. Kautz, and S. Birchfield, “Foundationpose: Unified 6d pose estimation and tracking of novel objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.