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Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms.
A syntactic approach to robot imitation learning using probabilistic activity grammars
K. Lee, Y. Su, T.-K. Kim, and Y. Demiris · 2013
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Robot learning manipulation action plans by” watching” unconstrained videos from the world wide web
Y. Yang, Y. Li, C. Fermuller, and Y. Aloimonos · 2015
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Learning robot activities from first-person human videos using convolutional future regression
J. Lee and M. S. Ryoo · 2017
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Scaling egocentric vision: The epic-kitchens dataset
D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray · 2018
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Translating videos to commands for robotic manipulation with deep recurrent neural networks
A. Nguyen, D. Kanoulas, L. Muratore, D. G. Caldwell, and N. G. Tsagarakis · 2018
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Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution
J. Rothfuss, F. Ferreira, E. E. Aksoy, Y. Zhou, and T. Asfour · 2018
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Robonet: Large-scale multi-robot learning
S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, S. Levine, and C. Finn · 2019
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Online object representations with contrastive learning
S. Pirk, M. Khansari, Y. Bai, C. Lynch, and P. Sermanet · 2019
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On the continuity of rotation representations in neural networks
Y. Zhou, C. Barnes, J. Lu, J. Yang, and H. Li · 2019
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One policy to control them all: Shared modular policies for agent-agnostic control
W. Huang, I. Mordatch, and D. Pathak · 2020
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Learning cross-domain correspondence for control with dynamics cycle-consistency
Q. Zhang, T. Xiao, A. A. Efros, L. Pinto, and X. Wang · 2020
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 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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Model-based inverse reinforcement learning from visual demonstrations
N. Das, S. Bechtle, T. Davchev, D. Jayaraman, A. Rai, and F. Meier · 2021
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Learning by watching: Physical imitation of manipulation skills from human videos
H. Xiong, Q. Li, Y.-C. Chen, H. Bharadhwaj, S. Sinha, and A. Garg · 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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Bayesian meta-learning for few-shot policy adaptation across robotic platforms
A. Ghadirzadeh, X. Chen, P. Poklukar, C. Finn, M. Björkman, and D. Kragic · 2021
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Policy transfer across visual and dynamics domain gaps via iterative grounding
G. Zhang, L. Zhong, Y. Lee, and J. J. Lim · 2021
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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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R3m: A universal visual representation for robot manipulation
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Y. J. Ma, S. Sodhani, D. Jayaraman, O. Bastani, V. Kumar, and A. Zhang · 2022
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Learn what matters: cross-domain imitation learning with task-relevant embeddings
T. Franzmeyer, P. Torr, and J. F. Henriques · 2022
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Translating robot skills: Learning unsupervised skill correspondences across robots
T. Shankar, Y. Lin, A. Rajeswaran, V. Kumar, S. Anderson, and J. Oh · 2022
Earlier work this paper cites.
Cross domain robot imitation with invariant representation
Z.-H. Yin, L. Sun, H. Ma, M. Tomizuka, and W.-J. Li · 2022
Cited alongside, same era.
Xirl: Cross-embodiment inverse reinforcement learning
K. Zakka, A. Zeng, P. Florence, J. Tompson, J. Bohg, and D. Dwibedi · 2022
Cited alongside, same era.
Human-to-robot imitation in the wild
S. Bahl, A. Gupta, and D. Pathak · 2022
Cited alongside, same era.
Learning fine-grained bimanual manipulation with low-cost hardware
T. Z. Zhao, V. Kumar, S. Levine, and C. Finn · 2023
Cited alongside, same era.
Open x-embodiment: Robotic learning datasets and rt-x models
A. O’Neill, A. Rehman, A. Gupta, A. Maddukuri, A. Gupta, A. Padalkar, A. Lee, A. Pooley, A. Gupta, A. Mandlekar, et al · 2023
Cited alongside, same era.
Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots
C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song · 2024
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Ace: A cross-platform visual-exoskeletons system for low-cost dexterous teleoperation
S. Yang, M. Liu, Y. Qin, R. Ding, J. Li, X. Cheng, R. Yang, S. Yi, and X. Wang · 2024
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Open-television: Teleoperation with immersive active visual feedback
X. Cheng, J. Li, S. Yang, G. Yang, and X. Wang · 2024
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Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning
T. He, Z. Luo, X. He, W. Xiao, C. Zhang, W. Zhang, K. Kitani, C. Liu, and G. Shi · 2024
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Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking
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OpenAI · 2023
Cited alongside, same era.
Structured world models from human videos
R. Mendonca, S. Bahl, and D. Pathak · 2023
Cited alongside, same era.
Affordances from human videos as a versatile representation for robotics
S. Bahl, R. Mendonca, L. Chen, U. Jain, and D. Pathak · 2023
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song · 2023
Cited alongside, same era.
Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto · 2023
Cited alongside, same era.
Any-point trajectory modeling for policy learning
C. Wen, X. Lin, J. So, K. Chen, Q. Dou, Y. Gao, and P. Abbeel · 2023
Cited alongside, same era.
Polybot: Training one policy across robots while embracing variability
J. Yang, D. Sadigh, and C. Finn · 2023
Cited alongside, same era.
H. Bharadhwaj, J. Vakil, M. Sharma, A. Gupta, S. Tulsiani, and V. Kumar · 2024
Later among the works it cites.
Umi on legs: Making manipulation policies mobile with manipulation-centric whole-body controllers
H. Ha, Y. Gao, Z. Fu, J. Tan, and S. Song · 2024
Later among the works it cites.
Dexcap: Scalable and portable mocap data collection system for dexterous manipulation
C. Wang, H. Shi, W. Wang, R. Zhang, L. Fei-Fei, and C. K. Liu · 2024
Later among the works it cites.
Egomimic: Scaling imitation learning via egocentric video
S. Kareer, D. Patel, R. Punamiya, P. Mathur, S. Cheng, C. Wang, J. Hoffman, and D. Xu · 2024
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Aloha unleashed: A simple recipe for robot dexterity
T. Z. Zhao, J. Tompson, D. Driess, P. Florence, K. Ghasemipour, C. Finn, and A. Wahid · 2024
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Scaling proprioceptive-visual learning with heterogeneous pre-trained transformers
L. Wang, X. Chen, J. Zhao, and K. He · 2024
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Wildlma: Long horizon loco-manipulation in the wild
R.-Z. Qiu, Y. Song, X. Peng, S. A. Suryadevara, G. Yang, M. Liu, M. Ji, C. Jia, R. Yang, X. Zou, et al · 2024
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Generalizable humanoid manipulation with improved 3d diffusion policies
Y. Ze, Z. Chen, W. Wang, T. Chen, X. He, Y. Yuan, X. B. Peng, and J. Wu · 2024
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Track2act: Predicting point tracks from internet videos enables diverse zero-shot robot manipulation
H. Bharadhwaj, R. Mottaghi, A. Gupta, and S. Tulsiani · 2024
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Okami: Teaching humanoid robots manipulation skills through single video imitation
J. Li, Y. Zhu, Y. Xie, Z. Jiang, M. Seo, G. Pavlakos, and Y. Zhu · 2024
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Flowretrieval: Flow-guided data retrieval for few-shot imitation learning
L.-H. Lin, Y. Cui, A. Xie, T. Hua, and D. Sadigh · 2024
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Vision-based manipulation from single human video with open-world object graphs
Y. Zhu, A. Lim, P. Stone, and Y. Zhu · 2024
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Spot: Se (3) pose trajectory diffusion for object-centric manipulation
C.-C. Hsu, B. Wen, J. Xu, Y. Narang, X. Wang, Y. Zhu, J. Biswas, and S. Birchfield · 2024
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Humanplus: Humanoid shadowing and imitation from humans
Z. Fu, Q. Zhao, Q. Wu, G. Wetzstein, and C. Finn · 2024
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Mirage: Cross-embodiment zero-shot policy transfer with cross-painting
L. Y. Chen, K. Hari, K. Dharmarajan, C. Xu, Q. Vuong, and K. Goldberg · 2024
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Pushing the limits of cross-embodiment learning for manipulation and navigation
J. Yang, C. Glossop, A. Bhorkar, D. Shah, Q. Vuong, C. Finn, D. Sadigh, and S. Levine · 2024
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Droid: A large-scale in-the-wild robot manipulation dataset
A. Khazatsky, K. Pertsch, S. Nair, A. Balakrishna, S. Dasari, S. Karamcheti, S. Nasiriany, M. K. Srirama, L. Y. Chen, K. Ellis, et al · 2024
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Mobile-television: Predictive motion priors for humanoid whole-body control
C. Lu, X. Cheng, J. Li, S. Yang, M. Ji, C. Yuan, G. Yang, S. Yi, and X. Wang · 2025
Closest in time.
Motion tracks: A unified representation for human-robot transfer in few-shot imitation learning
J. Ren, P. Sundaresan, D. Sadigh, S. Choudhury, and J. Bohg · 2025
Closest in time.