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Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world.
A unified approach for motion and force control of robot manipulators: The operational space formulation
O. Khatib · 1987
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Alvinn: An autonomous land vehicle in a neural network
D. A. Pomerleau · 1988
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Mixture density networks
C. M. Bishop · 1994
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Is imitation learning the route to humanoid robots?
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Movement imitation with nonlinear dynamical systems in humanoid robots
A. Ijspeert, J. Nakanishi, and S. Schaal · 2002
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Dynamic movement primitives-a framework for motor control in humans and humanoid robotics
S. Schaal · 2006
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Robot programming by demonstration
A. Billard, S. Calinon, R. Dillmann, and S. Schaal · 2008
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Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
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A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
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Learning motor primitives for robotics
J. Kober and J. Peters · 2009
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Learning and reproduction of gestures by imitation
S. Calinon, F. D’halluin, E. L. Sauser, D. G. Caldwell, and A. G. Billard · 2010
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Imitation and reinforcement learning
J. Kober and J. Peters · 2010
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Long short-term memory
A. Graves and A. Graves · 2012
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Probabilistic movement primitives
A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann · 2013
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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One-shot visual imitation learning via meta-learning
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
T. Zhang, Z. McCarthy, O. Jow, D. Lee, X. Chen, K. Goldberg, and P. Abbeel · 2018
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Taco: Learning task decomposition via temporal alignment for control
K. Shiarlis, M. Wulfmeier, S. Salter, S. Whiteson, and I. Posner · 2018
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Learning manipulation skills from a single demonstration
P. Englert and M. Toussaint · 2018
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Using probabilistic movement primitives in robotics
A. Paraschos, C. Daniel, J. Peters, and G. Neumann · 2018
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Neural task programming: Learning to generalize across hierarchical tasks
D. Xu, S. Nair, Y. Zhu, J. Gao, A. Garg, L. Fei-Fei, and S. Savarese · 2018
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
Y. Liu, A. Gupta, P. Abbeel, and S. Levine · 2018
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Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al · 2018
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Self-supervised correspondence in visuomotor policy learning
P. Florence, L. Manuelli, and R. Tedrake · 2019
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Third-person visual imitation learning via decoupled hierarchical controller
P. Sharma, D. Pathak, and A. Gupta · 2019
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
Concept2robot: Learning manipulation concepts from instructions and human demonstrations
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg · 2021
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Learning generalizable robotic reward functions from” in-the-wild” human videos
A. S. Chen, S. Nair, and C. Finn · 2021
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Reinforcement learning with videos: Combining offline observations with interaction
K. Schmeckpeper, O. Rybkin, K. Daniilidis, S. Levine, and C. Finn · 2021
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From play to policy: Conditional behavior generation from uncurated robot data
Z. J. Cui, Y. Wang, N. Muhammad, L. Pinto, et al · 2022
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Latent plans for task-agnostic offline reinforcement learning
E. Rosete-Beas, O. Mees, G. Kalweit, J. Boedecker, and W. Burgard · 2022
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L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine · 2019
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Perceptual values from observation
A. D. Edwards and C. L. Isbell · 2019
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Temporal cycle-consistency learning
D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman · 2019
Cited alongside, same era.
Learning to generalize across long-horizon tasks from human demonstrations
A. Mandlekar, D. Xu, R. Martín-Martín, S. Savarese, and L. Fei-Fei · 2020
Cited alongside, same era.
Learning latent plans from play
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet · 2020
Cited alongside, same era.
Iris: Implicit reinforcement without interaction at scale for learning control from offline robot manipulation data
A. Mandlekar, F. Ramos, B. Boots, S. Savarese, L. Fei-Fei, A. Garg, and D. Fox · 2020
Cited alongside, same era.
Learning predictive models from observation and interaction
K. Schmeckpeper, A. Xie, O. Rybkin, S. Tian, K. Daniilidis, S. Levine, and C. Finn · 2020
Cited alongside, same era.
VIOLA: Object-centric imitation learning for vision-based robot manipulation
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu · 2022
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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, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K.-H. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. Ryoo, G. Salazar, P. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, and B. Zitkovich · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
brian ichter, A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, D. Kalashnikov, S. Levine, Y. Lu, C. Parada, K. Rao, P. Sermanet, A. T. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, M. Yan, N. Brown, M. Ahn, O. Cortes, N. Sievers, C. Tan, S. Xu, D. Reyes, J. Rettinghouse, J. Quiambao, P. Pastor, L. Luu, K.-H. Lee, Y. Kuang, S. Jesmonth, K. Jeffrey, R. J. Ruano, J. Hsu, K. Gopalakrishnan, B. David, A. Zeng, and C. K. Fu · 2022
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Xirl: Cross-embodiment inverse reinforcement learning
K. Zakka, A. Zeng, P. Florence, J. Tompson, J. Bohg, and D. Dwibedi · 2022
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Videodex: Learning dexterity from internet videos
K. Shaw, S. Bahl, and D. Pathak · 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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Masked visual pre-training for motor control
T. Xiao, I. Radosavovic, T. Darrell, and J. Malik · 2022
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Ego4d: Around the world in 3,000 hours of egocentric video
K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Hamburger, H. Jiang, M. Liu, X. Liu, et al · 2022
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On pre-training for visuo-motor control: Revisiting a learning-from-scratch baseline
N. Hansen, Z. Yuan, Y. Ze, T. Mu, A. Rajeswaran, H. Su, H. Xu, and X. Wang · 2022
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Graph inverse reinforcement learning from diverse videos
S. Kumar, J. Zamora, N. Hansen, R. Jangir, and X. Wang · 2022
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Human-to-robot imitation in the wild
S. Bahl, A. Gupta, and D. Pathak · 2022
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Latent plans for task agnostic offline reinforcement learning
E. Rosete-Beas, O. Mees, G. Kalweit, J. Boedecker, and W. Burgard · 2022
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Behavior transformers: Cloning k k modes with one stone
N. M. M. Shafiullah, Z. J. Cui, A. Altanzaya, and L. Pinto · 2022
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Tclr: Temporal contrastive learning for video representation
I. Dave, R. Gupta, M. N. Rizve, and M. Shah · 2022
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Libero: Benchmarking knowledge transfer for lifelong robot learning, 2023
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone · 2023
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Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, et al · 2023
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