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Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error.
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Movement imitation with nonlinear dynamical systems in humanoid robots
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Bilateral teleoperation: An historical survey
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Robot programming by demonstration
A. Billard, S. Calinon, R. Dillmann, and S. Schaal · 2008
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Dynamical system modulation for robot learning via kinesthetic demonstrations
M. Hersch, F. Guenter, S. Calinon, and A. Billard · 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 and reproduction of gestures by imitation
S. Calinon, F. D’halluin, E. L. Sauser, D. G. Caldwell, and A. Billard · 2010
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Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts
K. Hauser and V. Ng-Thow-Hing · 2010
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Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input
P. Kormushev, S. Calinon, and D. G. Caldwell · 2011
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Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective
B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz · 2012
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Incremental semantically grounded learning from demonstration
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
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{TRAC-IK}: An open-source library for improved solving of generic inverse kinematics
P. Beeson and B. Ames · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Asymptotically optimal planning under piecewise-analytic constraints
W. Vega-Brown and N. Roy · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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Query-efficient imitation learning for end-to-end autonomous driving
J. Zhang and K. Cho · 2016
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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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Revisiting unreasonable effectiveness of data in deep learning era
C. Sun, A. Shrivastava, S. Singh, and A. Gupta · 2017
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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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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
T. Zhang, Z. McCarthy, O. Jow, D. Lee, K. Goldberg, and P. Abbeel · 2017
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Learning heuristic search via imitation
M. Bhardwaj, S. Choudhury, and S. Scherer · 2017
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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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Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
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Differentiable physics and stable modes for tool-use and manipulation planning
M. A. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum · 2018
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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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Reinforcement and imitation learning for diverse visuomotor skills
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, et al · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Skew-fit: State-covering self-supervised reinforcement learning
V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, and S. Levine · 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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Motion planning networks
A. H. Qureshi, A. Simeonov, M. J. Bency, and M. C. Yip · 2019
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Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks
R. Martín-Martín, M. A. Lee, R. Gardner, S. Savarese, J. Bohg, and A. Garg · 2019
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Palm: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2022
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Lamda: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, et al · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, et al · 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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A. Mandlekar, J. Booher, M. Spero, A. Tung, A. Gupta, Y. Zhu, A. Garg, S. Savarese, and L. Fei-Fei · 2019
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Hg-dagger: Interactive imitation learning with human experts
M. Kelly, C. Sidrane, K. Driggs-Campbell, and M. J. Kochenderfer · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al · 2020
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Learning latent plans from play
C. Lynch, M. Khansari, T. Xiao, V. Kumar, J. Tompson, S. Levine, and P. Sermanet · 2020
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Language conditioned imitation learning over unstructured data
C. Lynch and P. Sermanet · 2020
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The ingredients of real-world robotic reinforcement learning
H. Zhu, J. Yu, A. Gupta, D. Shah, K. Hartikainen, A. Singh, V. Kumar, and S. Levine · 2020
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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.
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Do as i can and 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, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. J. Ruano, K. Jeffrey, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, K.-H. Lee, S. Levine, Y. Lu, L. Luu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Rettinghouse, D. Reyes, P. Sermanet, N. Sievers, C. Tan, A. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, S. Xu, M. Yan, and A. Zeng · 2022
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S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, et al · 2022
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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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Bc-z: Zero-shot task generalization with robotic imitation learning
E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn · 2022
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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 · 2022
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Guided imitation of task and motion planning
M. J. McDonald and D. Hadfield-Menell · 2022
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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, K. Gopalakrishnan, K. Hausman, A. Herzog, et al · 2022
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Perceiver-actor: A multi-task transformer for robotic manipulation
M. Shridhar, L. Manuelli, and D. Fox · 2022
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Vima: General robot manipulation with multimodal prompts
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan · 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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A. Fishman, A. Murali, C. Eppner, B. Peele, B. Boots, and D. Fox · 2022
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Vision-based manipulators need to also see from their hands
K. Hsu, M. J. Kim, R. Rafailov, J. Wu, and C. Finn · 2022
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Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances
A. Curtis, X. Fang, L. P. Kaelbling, T. Lozano-Pérez, and C. R. Garrett · 2022
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Guided skill learning and abstraction for long-horizon manipulation
S. Cheng and D. Xu · 2022
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Megapose: 6d pose estimation of novel objects via render & compare
Y. Labbé, L. Manuelli, A. Mousavian, S. Tyree, S. Birchfield, J. Tremblay, J. Carpentier, M. Aubry, D. Fox, and J. Sivic · 2022
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Legged locomotion in challenging terrains using egocentric vision
A. Agarwal, A. Kumar, J. Malik, and D. Pathak · 2022
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Instruction-driven history-aware policies for robotic manipulations
P.-L. Guhur, S. Chen, R. Garcia, M. Tapaswi, I. Laptev, and C. Schmid · 2022
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Practical imitation learning in the real world via task consistency loss
M. Khansari, D. Ho, Y. Du, A. Fuentes, M. Bennice, N. Sievers, S. Kirmani, Y. Bai, and E. Jang · 2022
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Modem: Accelerating visual model-based reinforcement learning with demonstrations
N. Hansen, Y. Lin, H. Su, X. Wang, V. Kumar, and A. Rajeswaran · 2022
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Transformer in transformer as backbone for deep reinforcement learning
H. Mao, R. Zhao, H. Chen, J. Hao, Y. Chen, D. Li, J. Zhang, and Z. Xiao · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré · 2022
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Error-aware imitation learning from teleoperation data for mobile manipulation
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín · 2022
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Pato: Policy assisted teleoperation for scalable robot data collection
S. Dass, K. Pertsch, H. Zhang, Y. Lee, J. J. Lim, and S. Nikolaidis · 2022
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Learning models as functionals of signed-distance fields for manipulation planning
D. Driess, J.-S. Ha, M. Toussaint, and R. Tedrake · 2022
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Human-in-the-loop task and motion planning for imitation learning
A. Mandlekar, C. Garrett, D. Xu, and D. Fox · 2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 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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