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Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies.
N. Das, S. Bechtle, T. Davchev, D. Jayaraman, A. Rai, and F. Meier, “Model-based inverse reinforcement learning from visual demonstrations,” in Conference on Robot Learning , 2021, pp. 1930–1942
1942
Earlier work this paper cites.
D. A. Pomerleau, “Efficient training of artificial neural networks for autonomous navigation,” Neural computation , vol. 3, no. 1, pp. 88–97, 1991
1991
Earlier work this paper cites.
A. Y. Ng, D. Harada, and S. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” in International Conference on Machine Learning , vol. 99, 1999, pp. 278–287
1999
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in Neural Information Processing Systems , vol. 29, pp. 4565–4573, 2016
2016
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2017, pp. 23–30
2017
Earlier work this paper cites.
R. Goyal, S. Ebrahimi Kahou, V. Michalski, J. Materzynska, S. Westphal, H. Kim, V. Haenel, I. Fruend, P. Yianilos, M. Mueller-Freitag et al. , “The ”something something” video database for learning and evaluating visual common sense,” in Proceedings of the IEEE international Conference on Computer Vision , 2017, pp. 5842–5850
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,” The International journal of robotics research , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Earlier work this paper cites.
T. Yu, C. Finn, A. Xie, S. Dasari, T. Zhang, P. Abbeel, and S. Levine, “One-shot imitation from observing humans via domain-adaptive meta-learning,” in RSS , 2018
2018
Earlier work this paper cites.
P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, S. Levine, and G. Brain, “Time-contrastive networks: Self-supervised learning from video,” in IEEE International Conference on Robotics and Automation , 2018, pp. 1134–1141
2018
Earlier work this paper cites.
F. Torabi, G. Warnell, and P. Stone, “Behavioral cloning from observation,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , 2018, pp. 4950–4957
2018
Earlier work this paper cites.
Y. Aytar, T. Pfaff, D. Budden, T. Paine, Z. Wang, and N. De Freitas, “Playing hard exploration games by watching youtube,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International Conference on Machine Learning . PMLR, 2018, pp. 1861–1870
2018
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
Earlier work this paper cites.
Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang et al. , “Streaming end-to-end speech recognition for mobile devices,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 6381–6385
2019
Cited alongside, same era.
A. Kumar, J. Fu, M. Soh, G. Tucker, and S. Levine, “Stabilizing off-policy q-learning via bootstrapping error reduction,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
I. Kostrikov, K. K. Agrawal, D. Dwibedi, S. Levine, and J. Tompson, “Discriminator-actor-critic: Addressing sample inefficiency and reward bias in adversarial imitation learning,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
2019
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg, “Concept2robot: Learning manipulation concepts from instructions and human demonstrations,” The International Journal of Robotics Research , vol. 40, no. 12-14, pp. 1419–1434, 2021
2021
Later among the works it cites.
V. Petrík, M. Tapaswi, I. Laptev, and J. Sivic, “Learning object manipulation skills via approximate state estimation from real videos,” in Conference on Robot Learning . PMLR, 2021, pp. 296–312
2021
Later among the works it cites.
H. Xiong, Q. Li, Y.-C. Chen, H. Bharadhwaj, S. Sinha, and A. Garg, “Learning by watching: Physical imitation of manipulation skills from human videos,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2021, pp. 7827–7834
2021
Later among the works it cites.
J. Li, T. Lu, X. Cao, Y. Cai, and S. Wang, “Meta-imitation learning by watching video demonstrations,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
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Cited alongside, same era.
2019
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in Neural Information Processing Systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 444–11 453
2020
Cited alongside, same era.
K. Schmeckpeper, O. Rybkin, K. Daniilidis, S. Levine, and C. Finn, “Reinforcement learning with videos: Combining offline observations with interaction,” in Conference on Robot Learning , 2020
2020
Cited alongside, same era.
A. Bonardi, S. James, and A. J. Davison, “Learning one-shot imitation from humans without humans,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3533–3539, 2020
2020
Cited alongside, same era.
Z. Wang, A. Novikov, K. Zolna, J. S. Merel, J. T. Springenberg, S. E. Reed, B. Shahriari, N. Siegel, C. Gulcehre, N. Heess et al. , “Critic regularized regression,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Cited alongside, same era.
K. Hartikainen, X. Geng, T. Haarnoja, and S. Levine, “Dynamical distance learning for semi-supervised and unsupervised skill discovery,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in Conference on Robot Learning . PMLR, 2020, pp. 1094–1100
2020
Cited alongside, same era.
A. Arnab, M. Dehghani, G. Heigold, C. Sun, M. Lučić, and C. Schmid, “Vivit: A video vision transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6836–6846
2021
Later among the works it cites.
2022
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K. Zakka, A. Zeng, P. Florence, J. Tompson, J. Bohg, and D. Dwibedi, “Xirl: Cross-embodiment inverse reinforcement learning,” in Conference on Robot Learning . PMLR, 2022, pp. 537–546
2022
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E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “Bc-z: Zero-shot task generalization with robotic imitation learning,” in Conference on Robot Learning . PMLR, 2022, pp. 991–1002
2022
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S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3m: A universal visual representation for robot manipulation,” in Conference on Robot Learning , 2022
2022
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Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang, “Dexmv: Imitation learning for dexterous manipulation from human videos,” in ECCV 2022 . Springer, 2022, pp. 570–587
2022
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K. Grauman, A. Westbury, E. Byrne, Z. Chavis, A. Furnari, R. Girdhar, J. Hamburger, H. Jiang, M. Liu, X. Liu et al. , “Ego4d: Around the world in 3,000 hours of egocentric video,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 995–19 012
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Scenic: A JAX library for computer vision research and beyond
Mostafa Dehghani, Alexey Gritsenko, Anurag Arnab, Matthias Minderer, and Yi Tay · 2022
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Bridge data: Boosting generalization of robotic skills with cross-domain datasets
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S. Fujimoto, D. Meger, and D. Precup, “Off-policy deep reinforcement learning without exploration,” in International Conference on Machine Learning , 2019, pp. 2052–2062
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