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Recent work has demonstrated that a promising strategy for teaching robots a wide range of complex skills is by training them on a curriculum of progressively more challenging environments.
Evolutionary robotics
D. Floreano, P. Husbands, and S. Nolfi · 2008
Earlier work this paper cites.
Exploration in model-based reinforcement learning by empirically estimating learning progress
M. Lopes, T. Lang, M. Toussaint, and P.-Y. Oudeyer · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Reverse curriculum generation for reinforcement learning
C. Florensa, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Earlier work this paper cites.
Emergent complexity via multi-agent competition, 2018
T. Bansal, J. Pachocki, S. Sidor, I. Sutskever, and I. Mordatch · 2018
Earlier work this paper cites.
Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
Earlier work this paper cites.
Automatic goal generation for reinforcement learning agents
C. Florensa, D. Held, X. Geng, and P. Abbeel · 2018
Earlier work this paper cites.
R. Wang, J. Lehman, J. Clune, and K. O. Stanley · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Emergent tool use from multi-agent autocurricula, 2020
B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch · 2020
Earlier work this paper cites.
Emergent complexity and zero-shot transfer via unsupervised environment design
M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine · 2020
Earlier work this paper cites.
Enhanced poet: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions
R. Wang, J. Lehman, A. Rawal, J. Zhi, Y. Li, J. Clune, and K. Stanley · 2020
Earlier work this paper cites.
Embodied intelligence via learning and evolution
A. Gupta, S. Savarese, S. Ganguli, and L. Fei-Fei · 2021
Earlier work this paper cites.
Learning agile motor skills on quadrupedal robots using curriculum learning
Z. Tang, D. Kim, and S. Ha · 2021
Earlier work this paper cites.
Guided curriculum learning for walking over complex terrain, 2021
B. Tidd, N. Hudson, and A. Cosgun · 2021
Earlier work this paper cites.
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft, 2021
I. Kanitscheider, J. Huizinga, D. Farhi, W. H. Guss, B. Houghton, R. Sampedro, P. Zhokhov, B. Baker, A. Ecoffet, J. Tang, O. Klimov, and J. Clune · 2021
Earlier work this paper cites.
Curriculum learning for safe mapless navigation
L. Marzari, D. Corsi, E. Marchesini, and A. Farinelli · 2022
Earlier work this paper cites.
Learning dynamic bipedal walking across stepping stones, 2022
H. Duan, A. Malik, M. S. Gadde, J. Dao, A. Fern, and J. Hurst · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago, T. Hubert, P. Choy, C. de Masson d’Autume, I. Babuschkin, X. Chen, P.-S. Huang, J. Welbl, S. Gowal, A. Cherepanov, J. Molloy, D. J. Mankowitz, E. Sutherland Robson, P. Kohli, N. de Freitas, K. Kavukcuoglu, and O. Vinyals · 2022
Earlier work this paper cites.
Large language models can self-improve, 2022
J. Huang, S. S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu, and J. Han · 2022
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, et al · 2022
Cited alongside, same era.
Robot learning from randomized simulations: A review
F. Muratore, F. Ramos, G. Turk, W. Yu, M. Gienger, and J. Peters · 2022
Cited alongside, same era.
Evolving curricula with regret-based environment design
J. Parker-Holder, M. Jiang, M. Dennis, M. Samvelyan, J. Foerster, E. Grefenstette, and T. Rocktäschel · 2022
Cited alongside, same era.
Deep surrogate assisted generation of environments
V. Bhatt, B. Tjanaka, M. Fontaine, and S. Nikolaidis · 2022
Cited alongside, same era.
Rapid locomotion via reinforcement learning
G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal · 2022
Cited alongside, same era.
Text2motion: From natural language instructions to feasible plans
K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg · 2023
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Translating natural language to planning goals with large-language models
Y. Xie, C. Yu, T. Zhu, J. Bai, Z. Gong, and H. Soh · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al · 2023
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Large language models as generalizable policies for embodied tasks
A. Szot, M. Schwarzer, H. Agrawal, B. Mazoure, W. Talbott, K. Metcalf, N. Mackraz, D. Hjelm, and A. Toshev · 2023
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Legged locomotion in challenging terrains using egocentric vision, 2022
A. Agarwal, A. Kumar, J. Malik, and D. Pathak · 2022
Cited alongside, same era.
Deep whole-body control: Learning a unified policy for manipulation and locomotion, 2022
Z. Fu, X. Cheng, and D. Pathak · 2022
Cited alongside, same era.
Intrinsically motivated goal exploration processes with automatic curriculum learning
S. Forestier, R. Portelas, Y. Mollard, and P.-Y. Oudeyer · 2022
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis, 2023
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong · 2023
Cited alongside, same era.
Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao · 2023
Cited alongside, same era.
Extreme parkour with legged robots
X. Cheng, K. Shi, A. Agarwal, and D. Pathak · 2023
Cited alongside, same era.
Grounded decoding: Guiding text generation with grounded models for robot control
W. Huang, F. Xia, D. Shah, D. Driess, A. Zeng, Y. Lu, P. Florence, I. Mordatch, S. Levine, K. Hausman, et al · 2023
Cited alongside, same era.
Y. Tang, W. Yu, J. Tan, H. Zen, A. Faust, and T. Harada · 2023
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Scaling up and distilling down: Language-guided robot skill acquisition
H. Ha, P. Florence, and S. Song · 2023
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Language to rewards for robotic skill synthesis
W. Yu, N. Gileadi, C. Fu, S. Kirmani, K.-H. Lee, M. G. Arenas, H.-T. L. Chiang, T. Erez, L. Hasenclever, J. Humplik, et al · 2023
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Text2reward: Automated dense reward function generation for reinforcement learning
T. Xie, S. Zhao, C. H. Wu, Y. Liu, Q. Luo, V. Zhong, Y. Yang, and T. Yu · 2023
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Eureka: Human-level reward design via coding large language models
Y. J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y. Zhu, L. Fan, and A. Anandkumar · 2023
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Stabilizing unsupervised environment design with a learned adversary
I. Mediratta, M. Jiang, J. Parker-Holder, M. Dennis, E. Vinitsky, and T. Rocktäschel · 2023
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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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Neural volumetric memory for visual locomotion control
R. Yang, G. Yang, and X. Wang · 2023
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Barkour: Benchmarking animal-level agility with quadruped robots, 2023
K. Caluwaerts, A. Iscen, J. C. Kew, W. Yu, T. Zhang, D. Freeman, K.-H. Lee, L. Lee, S. Saliceti, V. Zhuang, N. Batchelor, S. Bohez, F. Casarini, J. E. Chen, O. Cortes, E. Coumans, A. Dostmohamed, G. Dulac-Arnold, A. Escontrela, E. Frey, R. Hafner, D. Jain, B. Jyenis, Y. Kuang, E. Lee, L. Luu, O. Nachum, K. Oslund, J. Powell, D. Reyes, F. Romano, F. Sadeghi, R. Sloat, B. Tabanpour, D. Zheng, M. Neunert, R. Hadsell, N. Heess, F. Nori, J. Seto, C. Parada, V. Sindhwani, V. Vanhoucke, and J. Tan · 2023
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Learning visual locomotion with cross-modal supervision
A. Loquercio, A. Kumar, and J. Malik · 2023
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Code llama: Open foundation models for code, 2024
B. Rozière, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, R. Sauvestre, T. Remez, J. Rapin, A. Kozhevnikov, I. Evtimov, J. Bitton, M. Bhatt, C. C. Ferrer, A. Grattafiori, W. Xiong, A. Défossez, J. Copet, F. Azhar, H. Touvron, L. Martin, N. Usunier, T. Scialom, and G. Synnaeve · 2024
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Query2cad: Generating cad models using natural language queries, 2024
A. Badagabettu, S. S. Yarlagadda, and A. B. Farimani · 2024
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Large language models can self-improve at web agent tasks, 2024
A. Patel, M. Hofmarcher, C. Leoveanu-Condrei, M.-C. Dinu, C. Callison-Burch, and S. Hochreiter · 2024
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Self-play fine-tuning converts weak language models to strong language models, 2024
Z. Chen, Y. Deng, H. Yuan, K. Ji, and Q. Gu · 2024
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Dreureka: Language model guided sim-to-real transfer
Y. J. Ma, W. Liang, H. Wang, S. Wang, Y. Zhu, L. Fan, O. Bastani, and D. Jayaraman · 2024
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Anymal parkour: Learning agile navigation for quadrupedal robots
D. Hoeller, N. Rudin, D. Sako, and M. Hutter · 2024
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Quadruped robot traversing 3d complex environments
Y. Cheng, H. Liu, G. Pan, L. Ye, H. Liu, and B. Liang · 2024
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