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How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion information from web data through human video generation and conditioning a robot policy on the generated video.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, and M. Hebert, “Learning monocular reactive uav control in cluttered natural environments,” in 2013 IEEE international conference on robotics and automation . IEEE, 2013, pp. 1765–1772
2013
Earlier work this paper cites.
C. Finn, T. Yu, T. Zhang, P. Abbeel, and S. Levine, “One-shot visual imitation learning via meta-learning,” in Conference on robot learning . PMLR, 2017, pp. 357–368
2017
Earlier work this paper cites.
Z. Chen and X. Huang, “End-to-end learning for lane keeping of self-driving cars,” in 2017 IEEE intelligent vehicles symposium (IV) . IEEE, 2017, pp. 1856–1860
2017
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Earlier work this paper cites.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, et al. , “Roboturk: A crowdsourcing platform for robotic skill learning through imitation,” in Conference on Robot Learning . PMLR, 2018, pp. 879–893
2018
Earlier work this paper cites.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al. , “Scalable deep reinforcement learning for vision-based robotic manipulation,” in Conference on robot learning . PMLR, 2018, pp. 651–673
2018
Earlier work this paper cites.
L. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine, “Avid: Learning multi-stage tasks via pixel-level translation of human videos,” arXiv , 2019
2019
Earlier work this paper cites.
S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto, “Visual imitation made easy,” in Conference on Robot Learning (CoRL) , 2020
2020
Earlier work this paper 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,” arXiv , 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
K. Mo, L. J. Guibas, M. Mukadam, A. Gupta, and S. Tulsiani, “Where2act: From pixels to actions for articulated 3d objects,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6813–6823
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
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
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
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
Earlier work this paper cites.
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
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
M. Goyal, S. Modi, R. Goyal, and S. Gupta, “Human hands as probes for interactive object understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3293–3303
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Bahl, R. Mendonca, L. Chen, U. Jain, and D. Pathak, “Affordances from human videos as a versatile representation for robotics,” in CVPR , 2023
2023
Later among the works it cites.
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S. Liu, S. Tripathi, S. Majumdar, and X. Wang, “Joint hand motion and interaction hotspots prediction from egocentric videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3282–3292
2022
Cited alongside, same era.
S. Bahl, A. Gupta, and D. Pathak, “Human-to-robot imitation in the wild,” RSS , 2022
2022
Cited alongside, same era.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, et al. , “Flamingo: a visual language model for few-shot learning,” Advances in neural information processing systems , vol. 35, pp. 23 716–23 736, 2022
2022
Cited alongside, same era.
C. Doersch, A. Gupta, L. Markeeva, A. Recasens, L. Smaira, Y. Aytar, J. Carreira, A. Zisserman, and Y. Yang, “Tap-vid: A benchmark for tracking any point in a video,” Advances in Neural Information Processing Systems , vol. 35, pp. 13 610–13 626, 2022
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid, et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in Conference on Robot Learning . PMLR, 2023, pp. 2165–2183
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V. Kumar, “Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
Closest in time.
2024
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N. D. Palo and E. Johns, “Dinobot: Robot manipulation via retrieval and alignment with vision foundation models,” in IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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2024
Closest in time.
H. Bharadhwaj, A. Gupta, V. Kumar, and S. Tulsiani, “Towards generalizable zero-shot manipulation via translating human interaction plans,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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2024
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2024
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Y. Du, S. Yang, B. Dai, H. Dai, O. Nachum, J. Tenenbaum, D. Schuurmans, and P. Abbeel, “Learning universal policies via text-guided video generation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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G. Le Moing, J. Ponce, and C. Schmid, “Dense optical tracking: connecting the dots,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 187–19 197
2024
Closest in time.