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Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion.
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NVIDIA, “FleX: A particle-based simulation library,” May 2017. [Online]. Available: https://github.com/NVIDIAGameWorks/FleX
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B. Thananjeyan, A. Garg, S. Krishnan, C. Chen, L. Miller, and K. Goldberg, “Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 2371–2378
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S. Krishnan, A. Garg, S. Patil, C. Lea, G. Hager, P. Abbeel, and K. Goldberg, “Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning,” International Journal of Robotics Research (IJRR) , 2017
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J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg, “Learning ambidextrous robot grasping policies,” Science Robotics , vol. 4, no. 26, p. eaau4984, 2019
2019
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2019
Cited alongside, same era.
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F. Ficuciello, G. Tamburrini, A. Arezzo, L. Villani, and B. Siciliano, “Autonomy in surgical robots and its meaningful human control,” Paladyn, Journal of Behavioral Robotics , vol. 10, no. 1, pp. 30–43, 2019
2019
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M. Macklin and M. Muller, “A constraint-based formulation of stable neo-hookean materials,” in ACM SIGGRAPH Conference on Motion, Interaction and Games , New York, NY, USA, 2021
2021
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2021
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2021
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A. Munawar, Y. Wang, R. Gondokaryono, and G. S. Fischer, “A real-time dynamic simulator and an associated front-end representation format for simulating complex robots and environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1875–1882
2019
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E. Tagliabue, A. Pore, D. Dall’Alba, E. Magnabosco, M. Piccinelli, and P. Fiorini, “Soft tissue simulation environment to learn manipulation tasks in autonomous robotic surgery,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 3261–3266
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. James, Z. Ma, D. Rovick Arrojo, and A. J. Davison, “RLBench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters , 2020
2020
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X. Lin, Y. Wang, J. Olkin, and D. Held, “SoftGym: Benchmarking deep reinforcement learning for deformable object manipulation,” in Conference on Robot Learning (CoRL) . PMLR, 2020
2020
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T. D. Nagy and T. Haidegger, “Autonomous surgical robotics at task and subtask levels,” in Advanced Robotics and Intelligent Automation in Manufacturing . IGI global, 2020, pp. 296–319
2020
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P. Kaur, S. Taghavi, Z. Tian, and W. Shi, “A survey on simulators for testing self-driving cars,” in 2021 Fourth International Conference on Connected and Autonomous Driving (MetroCAD) . IEEE, 2021, pp. 62–70
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Allshire, M. MittaI, V. Lodaya, V. Makoviychuk, D. Makoviichuk, F. Widmaier, M. Wüthrich, S. Bauer, A. Handa, and A. Garg, “Transferring dexterous manipulation from GPU simulation to a remote real-world trifinger,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 11 802–11 809
2022
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2022
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N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 91–100
2022
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V. M. Varier, D. K. Rajamani, F. Tavakkolmoghaddam, A. Munawar, and G. S. Fischer, “AMBF-RL: A real-time simulation based reinforcement learning toolkit for medical robotics,” in 2022 International Symposium on Medical Robotics (ISMR) . IEEE, 2022, pp. 1–8
2022
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2022
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K. Goldberg. (2023) Augmented Dexterity: How Robots Can Enhance Surgeon Dexterity. [Online]. Available: https://bit.ly/Augmented-Dexterity-S24
2023
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M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar et al. , “ORBIT: A unified simulation framework for interactive robot learning environments,” IEEE Robotics and Automation Letters , 2023
2023
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A. T. Bourdillon, A. Garg, H. Wang, Y. J. Woo, M. Pavone, and J. Boyd, “Integration of reinforcement learning in a virtual robotic surgical simulation,” Surgical Innovation , vol. 30, no. 1, pp. 94–102, 2023
2023
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2023
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2023
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E. Heiden, M. Macklin, Y. Narang, D. Fox, A. Garg, and F. Ramos, “Disect: a differentiable simulator for parameter inference and control in robotic cutting,” Autonomous Robots , vol. 47, no. 5, pp. 549–578, 2023
2023
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