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We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
O. Khatib, “Inertial properties in robotic manipulation: An object-level framework,” The International Journal of Robotics Research , vol. 14, no. 1, 1995
1995
Earlier work this paper cites.
S. R. Buss and J.-S. Kim, “Selectively damped least squares for inverse kinematics,” Journal of Graphics Tools , vol. 10, 2005
2005
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng, et al. , “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, 2009
2009
Earlier work this paper cites.
S. G. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison, and M. Stich, “Optix: A general purpose ray tracing engine,” ACM Transactions On Graphics , vol. 29, no. 4, 2010
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2012
2012
Earlier work this paper cites.
P. Hintjens, ZeroMQ: messaging for many applications . " O’Reilly Media, Inc.", 2013
2013
Earlier work this paper cites.
M. Macklin and M. Müller, “Position based fluids,” ACM Transactions on Graphics , vol. 32, no. 4, 2013
2013
Earlier work this paper cites.
M. Macklin, M. Müller, N. Chentanez, and T.-Y. Kim, “Unified particle physics for real-time applications,” ACM Trans. Graph. , vol. 33, no. 4, jul 2014
2014
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning (CoRL) . PMLR, 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra, “Habitat: A Platform for Embodied AI Research,” in IEEE/CVF International Conference on Computer Vision (ICCV) , 2019
2019
Earlier work this paper cites.
M. Macklin, K. Storey, M. Lu, P. Terdiman, N. Chentanez, S. Jeschke, and M. Müller, “Small steps in physics simulation,” in ACM SIGGRAPH/Eurographics Symposium on Computer Animation , 2019
2019
Earlier work this paper cites.
R. Martín-Martín, M. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg, “Variable impedance control in end-effector space. an action space for reinforcement learning in contact rich tasks,” in IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS) , 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on Robot Learning (CoRL) . PMLR, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C.-A. Cheng, M. Mukadam, J. Issac, S. Birchfield, D. Fox, B. Boots, and N. Ratliff, “Rmpflow: A geometric framework for generation of multitask motion policies,” IEEE Transactions on Automation Science and Engineering , vol. 18, no. 3, 2021
2021
Later among the works it cites.
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” Journal of Machine Learning Research , 2021
2021
Later among the works it cites.
E. Ménager, P. Schegg, E. Khairallah, D. Marchal, J. Dequidt, P. Preux, and C. Duriez, “SofaGym: An open platform for Reinforcement Learning based on Soft Robot simulations,” Soft Robotics , 2022
2022
Later among the works it cites.
Y. Narang, K. Storey, I. Akinola, M. Macklin, P. Reist, L. Wawrzyniak, Y. Guo, A. Moravanszky, G. State, M. Lu, et al. , “Factory: Fast contact for robotic assembly,” Robotics: Science and Systems (RSS) , 2022
2022
Later among the works it cites.
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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
Cited alongside, same era.
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
Cited alongside, same era.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, et al. , “Sapien: A simulated part-based interactive environment,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Cited alongside, same era.
R. Antonova, p. shi, H. Yin, Z. Weng, and D. Kragic, “Dynamic environments with deformable objects,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , J. Vanschoren and S. Yeung, Eds., vol. 1. Curran, 2021
2021
Cited alongside, same era.
C. Li, F. Xia, R. Martín-Martín, M. Lingelbach, S. Srivastava, B. Shen, K. E. Vainio, C. Gokmen, G. Dharan, T. Jain, A. Kurenkov, K. Liu, H. Gweon, J. Wu, L. Fei-Fei, and S. Savarese, “igibson 2.0: Object-centric simulation for robot learning of everyday household tasks,” in Conference on Robot Learning (CoRL) . PMLR, 2021
2021
Cited alongside, same era.
A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y. Zhao, J. Turner, N. Maestre, M. Mukadam, D. Chaplot, O. Maksymets, A. Gokaslan, V. Vondrus, S. Dharur, F. Meier, W. Galuba, A. Chang, Z. Kira, V. Koltun, J. Malik, M. Savva, and D. Batra, “Habitat 2.0: Training home assistants to rearrange their habitat,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021
2021
Cited alongside, same era.
C. Gan, J. Schwartz, S. Alter, D. Mrowca, M. Schrimpf, J. Traer, J. De Freitas, J. Kubilius, A. Bhandwaldar, N. Haber, M. Sano, K. Kim, E. Wang, M. Lingelbach, A. Curtis, K. Feigelis, D. Bear, D. Gutfreund, D. Cox, A. Torralba, J. J. DiCarlo, J. Tenenbaum, J. McDermott, and D. Yamins, “Threedworld: A platform for interactive multi-modal physical simulation,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , vol. 1. Curran, 2021
2021
Cited alongside, same era.
K. Ehsani, W. Han, A. Herrasti, E. VanderBilt, L. Weihs, E. Kolve, A. Kembhavi, and R. Mottaghi, “Manipulathor: A framework for visual object manipulation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
2021
Cited alongside, same era.
M. Mittal, D. Hoeller, F. Farshidian, M. Hutter, and A. Garg, “Articulated object interaction in unknown scenes with whole-body mobile manipulation,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022
2022
Later among the works it cites.
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 (CoRL) . PMLR, 2022
2022
Later among the works it cites.
D. Makoviichuk and V. Makoviychuk, “rl-games: A high-performance framework for reinforcement learning,” https://github.com/Denys88/rl_games , May 2022
2022
Later among the works it cites.
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martin-Martin, “What matters in learning from offline human demonstrations for robot manipulation,” in Conference on Robot Learning (CoRL) . PMLR, 2022
2022
Later among the works it cites.
M. Dubied, M. Y. Michelis, A. Spielberg, and R. K. Katzschmann, “Sim-to-real for soft robots using differentiable fem: Recipes for meshing, damping, and actuation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, 2022
2022
Later among the works it cites.
A. Allshire, M. Mittal, 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,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2022
2022
Later among the works it cites.
J. Gu, F. Xiang, X. Li, Z. Ling, X. Liu, T. Mu, Y. Tang, S. Tao, X. Wei, Y. Yao, X. Yuan, P. Xie, Z. Huang, R. Chen, and H. Su, “Maniskill2: A unified benchmark for generalizable manipulation skills,” in International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.
NVIDIA, “Isaac sim - robotics simulation and synthetic data generation,” https://developer.nvidia.com/isaac-sim , 2023, (accessed on May 2, 2023)
2023
Closest in time.
E. Coumans, , and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” https://pybullet.org/wordpress/ , 2023, (accessed on May 2, 2023)
2023
Closest in time.
NVIDIA, “Physx sdk,” https://developer.nvidia.com/physx-sdk , 2023, (accessed on May 2, 2023)
2023
Closest in time.