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Online Imitation Learning struggles with the gap between extensive online exploration space and limited expert trajectories, hindering efficient exploration due to inaccurate reward estimation.
From example study to problem solving: Smooth transitions help learning
U. H. M. Alexander Renkl, Robert K. Atkinson and R. Staley · 2002
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Assimilating complex information
E. Pollock, P. Chandler, and J. Sweller · 2002
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Two-frame motion estimation based on polynomial expansion
G. Farnebäck · 2003
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Imitation learning for locomotion and manipulation
N. Ratliff, J. A. Bagnell, and S. S. Srinivasa · 2007
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Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
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Imitation learning: A survey of learning methods
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne · 2017
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Imitation learning: a survey of learning methods
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne · 2017
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Deeply aggrevated: Differentiable imitation learning for sequential prediction
W. Sun, A. Venkatraman, G. J. Gordon, B. Boots, and J. A. Bagnell · 2017
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Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy · 2018
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Survey of imitation learning for robotic manipulation
B. Fang, S. Jia, D. Guo, M. Xu, S. Wen, and F. Sun · 2019
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
D. Brown, W. Goo, P. Nagarajan, and S. Niekum · 2019
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Optimal transport: Fast probabilistic approximation with exact solvers
M. Sommerfeld, J. Schrieber, Y. Zemel, and A. Munk · 2019
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Computational optimal transport: With applications to data science
G. Peyré, M. Cuturi, et al · 2019
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Wasserstein adversarial imitation learning
H. Xiao, M. Herman, J. Wagner, S. Ziesche, J. Etesami, and T. H. Linh · 2019
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Primal wasserstein imitation learning
R. Dadashi, L. Hussenot, M. Geist, and O. Pietquin · 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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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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Open x-embodiment: Robotic learning datasets and rt-x models
A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan, et al · 2023
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A survey of imitation learning: Algorithms, recent developments, and challenges
M. Zare, P. M. Kebria, A. Khosravi, and S. Nahavandi · 2023
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Language reward modulation for pretraining reinforcement learning
A. Adeniji, A. Xie, C. Sferrazza, Y. Seo, S. James, and P. Abbeel · 2023
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Code as policies: Language model programs for embodied control
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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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Bridge data: Boosting generalization of robotic skills with cross-domain datasets
F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine · 2021
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A survey of inverse reinforcement learning: Challenges, methods and progress
S. Arora and P. Doshi · 2021
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Rt-1: Robotics transformer for real-world control at scale
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, et al · 2022
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Imitation learning with sinkhorn distances
G. Papagiannis and Y. Li · 2022
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R3m: A universal visual representation for robot manipulation
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta · 2022
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Octo: An open-source generalist robot policy
Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, C. Xu, J. Luo, T. Kreiman, Y. Tan, D. Sadigh, C. Finn, and S. Levine · 2023
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J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 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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Universal visual decomposer: Long-horizon manipulation made easy
Z. Zhang, Y. Li, O. Bastani, A. Gupta, D. Jayaraman, Y. J. Ma, and L. Weihs · 2023
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Rt-h: Action hierarchies using language
S. Belkhale, T. Ding, T. Xiao, P. Sermanet, Q. Vuong, J. Tompson, Y. Chebotar, D. Dwibedi, and D. Sadigh · 2024
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Depth helps: Improving pre-trained rgb-based policy with depth information injection
X. Pang, W. Xia, Z. Wang, B. Zhao, D. Hu, D. Wang, and X. Li · 2024
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Kinematic-aware prompting for generalizable articulated object manipulation with llms
W. Xia, D. Wang, X. Pang, Z. Wang, B. Zhao, D. Hu, and X. Li · 2024
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Roboclip: One demonstration is enough to learn robot policies
S. Sontakke, J. Zhang, S. Arnold, K. Pertsch, E. Bıyık, D. Sadigh, C. Finn, and L. Itti · 2024
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Libero: Benchmarking knowledge transfer for lifelong robot learning
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