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Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes.
Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Mastering the game of go without human knowledge
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Dota 2 with large scale deep reinforcement learning
C. Berner, G. Brockman, B. Chan, V. Cheung, P. Dkebiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, et al · 2019
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Solving rubik’s cube with a robot hand
I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, et al · 2019
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Drake: Model-based design and verification for robotics, 2019
R. Tedrake and the Drake Development Team · 2019
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Bayessim: adaptive domain randomization via probabilistic inference for robotics simulators
F. Ramos, R. C. Possas, and D. Fox · 2019
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox · 2019
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis · 2019
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kpam: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. Florence, and R. Tedrake · 2019
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SAPIEN: A simulated part-based interactive environment
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2020
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Rlbench: The robot learning benchmark & learning environment
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison · 2020
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Sim-to-real transfer in deep reinforcement learning for robotics: a survey
W. Zhao, J. P. Queralta, and T. Westerlund · 2020
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Learning dexterous in-hand manipulation
O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al · 2020
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On the opportunities and risks of foundation models
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al · 2021
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What matters in learning from offline human demonstrations for robot manipulation
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
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Transporter networks: Rearranging the visual world for robotic manipulation
A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, et al · 2021
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Openclip, July 2021
G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2021
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K. Fang, T. Migimatsu, A. Mandlekar, L. Fei-Fei, and J. Bohg · 2022
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Procthor: Large-scale embodied ai using procedural generation
M. Deitke, E. VanderBilt, A. Herrasti, L. Weihs, K. Ehsani, J. Salvador, W. Han, E. Kolve, A. Kembhavi, and R. Mottaghi · 2022
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Inner monologue: Embodied reasoning through planning with language models
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, et al · 2022
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Point-e: A system for generating 3d point clouds from complex prompts
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen · 2022
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Progprompt: Generating situated robot task plans using large language models
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg · 2023
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2023
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Dextreme: Transfer of agile in-hand manipulation from simulation to reality
A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam, et al · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. Burchfiel, and S. Song · 2023
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B. Poole, A. Jain, J. T. Barron, and B. Mildenhall · 2022
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Goal-auxiliary actor-critic for 6d robotic grasping with point clouds
L. Wang, Y. Xiang, W. Yang, A. Mousavian, and D. Fox · 2022
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Cliport: What and where pathways for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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Pointnext: Revisiting pointnet++ with improved training and scaling strategies
G. Qian, Y. Li, H. Peng, J. Mai, H. Hammoud, M. Elhoseiny, and B. Ghanem · 2022
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Champion-level drone racing using deep reinforcement learning
E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza · 2023
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Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Code llama: Open foundation models for code
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al · 2023
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3daxiesprompts: Unleashing the 3d spatial task capabilities of gpt-4v
D. Liu, X. Dong, R. Zhang, X. Luo, P. Gao, X. Huang, Y. Gong, and Z. Wang · 2023
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Gnfactor: Multi-task real robot learning with generalizable neural feature fields
Y. Ze, G. Yan, Y.-H. Wu, A. Macaluso, Y. Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang · 2023
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Solving olympiad geometry without human demonstrations
T. H. Trinh, Y. Wu, Q. V. Le, H. He, and T. Luong · 2024
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Robocasa: Large-scale simulation of everyday tasks for generalist robots
S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu · 2024
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Video generation models as world simulators
T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh · 2024
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Diffusion models are real-time game engines, 2024
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Genie: Generative interactive environments
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Libero: Benchmarking knowledge transfer for lifelong robot learning
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone · 2024
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Reflexion: Language agents with verbal reinforcement learning
N. Shinn, F. Cassano, A. Gopinath, K. Narasimhan, and S. Yao · 2024
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Manipulate-anything: Automating real-world robots using vision-language models
J. Duan, W. Yuan, W. Pumacay, Y. R. Wang, K. Ehsani, D. Fox, and R. Krishna · 2024
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Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu · 2024
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Poco: Policy composition from and for heterogeneous robot learning
L. Wang, J. Zhao, Y. Du, E. H. Adelson, and R. Tedrake · 2024
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Moka: Open-vocabulary robotic manipulation through mark-based visual prompting
F. Liu, K. Fang, P. Abbeel, and S. Levine · 2024
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Keypoint action tokens enable in-context imitation learning in robotics
N. Di Palo and E. Johns · 2024
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Rekep: Spatio-temporal reasoning of relational keypoint constraints for robotic manipulation
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Scaling proprioceptive-visual learning with heterogeneous pre-trained transformers
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Blink: Multimodal large language models can see but not perceive
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Dnact: Diffusion guided multi-task 3d policy learning
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