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Recent advances in diffusion-based robot policies have demonstrated significant potential in imitating multi-modal behaviors.
B. O. Koopman, “Hamiltonian systems and transformation in hilbert space,” Proceedings of the National Academy of Sciences , vol. 17, no. 5, pp. 315–318, 1931
1931
Earlier work this paper cites.
B. O. Koopman and J. v. Neumann, “Dynamical systems of continuous spectra,” Proceedings of the National Academy of Sciences , vol. 18, no. 3, pp. 255–263, 1932
1932
Earlier work this paper cites.
2013
Earlier work this paper cites.
S. L. Brunton, B. W. Brunton, J. L. Proctor, and J. N. Kutz, “Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control,” PLoS ONE , vol. 11, 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:7675653
2015
Earlier work this paper cites.
S. L. Brunton, B. W. Brunton, J. L. Proctor, and J. N. Kutz, “Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control,” PloS one , vol. 11, no. 2, p. e0150171, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
B. Lusch, J. N. Kutz, and S. L. Brunton, “Deep learning for universal linear embeddings of nonlinear dynamics,” Nature Communications , vol. 9, 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:4854885
2017
Earlier work this paper cites.
A. van den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY, USA: Curran Associates Inc., 2017, p. 6309–6318
2017
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Y. Liu, A. Gupta, P. Abbeel, and S. Levine, “Imitation from observation: Learning to imitate behaviors from raw video via context translation,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 1118–1125
2018
Earlier work this paper cites.
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.
A. Edwards, H. Sahni, Y. Schroecker, and C. Isbell, “Imitating latent policies from observation,” in International conference on machine learning . PMLR, 2019, pp. 1755–1763
2019
Earlier work this paper cites.
F. Torabi, G. Warnell, and P. Stone, “Recent advances in imitation learning from observation,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 6325–6331. [Online]. Available: https://doi.org/10.24963/ijcai.2019/882
2019
Earlier work this paper cites.
P. Sharma, D. Pathak, and A. Gupta, “Third-person visual imitation learning via decoupled hierarchical controller,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
K. Schmeckpeper, A. Xie, O. Rybkin, S. Tian, K. Daniilidis, S. Levine, and C. Finn, “Learning predictive models from observation and interaction,” in European Conference on Computer Vision , 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:209515451
2019
Cited alongside, same era.
D. Bruder, B. Gillespie, C. David Remy, and R. Vasudevan, “Modeling and control of soft robots using the koopman operator and model predictive control,” Robotics: Science and Systems XV , Jun. 2019. [Online]. Available: http://www.roboticsproceedings.org/rss15/p60.pdf
2019
Cited alongside, same era.
G. Mamakoukas, M. L. Castaño, X. Tan, and T. D. Murphey, “Local koopman operators for data-driven control of robotic systems,” Robotics: Science and Systems XV , 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:197640648
X. Lyu, H. Hu, S. Siriya, Y. Pu, and M. Chen, “Task-oriented koopman-based control with contrastive encoder,” in Conference on Robot Learning . PMLR, 2023, pp. 93–105
2023
Later among the works it cites.
Q. Zheng, M. Henaff, B. Amos, and A. Grover, “Semi-supervised offline reinforcement learning with action-free trajectories,” in International conference on machine learning . PMLR, 2023, pp. 42 339–42 362
2023
Later among the works it cites.
C. Chi, S. Feng, Y. Du, Z. Xu, E. Cousineau, B. C. Burchfiel, and S. Song, “Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,” in Proceedings of Robotics: Science and Systems , Daegu, Republic of Korea, July 2023
2023
Later among the works it cites.
X. Lyu, H. Hu, S. Siriya, Y. Pu, and M. Chen, “Task-oriented koopman-based control with contrastive encoder,” in 7th Annual Conference on Robot Learning , 2023. [Online]. Available: https://openreview.net/forum?id=q0VAoefCI2
2023
Later among the works it cites.
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2019
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Li, H. He, J. Wu, D. Katabi, and A. Torralba, “Learning compositional koopman operators for model-based control,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=H1ldzA4tPr
2020
Cited alongside, same era.
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 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 7827–7834
2021
Cited alongside, same era.
I. Radosavovic, X. Wang, L. Pinto, and J. Malik, “State-only imitation learning for dexterous manipulation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 7865–7871
2021
Cited alongside, same era.
S. Nair, A. Rajeswaran, V. Kumar, C. Finn, and A. Gupta, “R3m: A universal visual representation for robot manipulation,” in 6th Annual Conference on Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=tGbpgz6yOrI
2022
Cited alongside, same era.
Y. Seo, K. Lee, S. L. James, and P. Abbeel, “Reinforcement learning with action-free pre-training from videos,” in International Conference on Machine Learning . PMLR, 2022, pp. 19 561–19 579
2022
Cited alongside, same era.
B. Baker, I. Akkaya, P. Zhokov, J. Huizinga, J. Tang, A. Ecoffet, B. Houghton, R. Sampedro, and J. Clune, “Video pretraining (vpt): Learning to act by watching unlabeled online videos,” Advances in Neural Information Processing Systems , vol. 35, pp. 24 639–24 654, 2022
2022
Cited alongside, same era.
M. Janner, Y. Du, J. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” in International Conference on Machine Learning , 2022
2022
Cited alongside, same era.
Y. Han, M. Xie, Y. Zhao, and H. Ravichandar, “On the utility of koopman operator theory in learning dexterous manipulation skills,” in 7th Annual Conference on Robot Learning , 2023. [Online]. Available: https://openreview.net/forum?id=pw-OTIYrGa
2023
Later among the works it cites.
Z. Liang, W. Hao, and S. Mou, “A data-driven approach for inverse optimal control,” 2023 62nd IEEE Conference on Decision and Control (CDC) , pp. 3632–3637, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:257913681
2023
Later among the works it cites.
D. Schmidt and M. Jiang, “Learning to act without actions,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=rvUq3cxpDF
2024
Closest in time.
X. Jia, D. Blessing, X. Jiang, M. Reuss, A. Donat, R. Lioutikov, and G. Neumann, “Towards diverse behaviors: A benchmark for imitation learning with human demonstrations,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=6pPYRXKPpw
2024
Closest in time.
M. Zare, P. M. Kebria, A. Khosravi, and S. Nahavandi, “A survey of imitation learning: Algorithms, recent developments, and challenges,” IEEE Transactions on Cybernetics , 2024
2024
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F. Mentzer, D. Minnen, E. Agustsson, and M. Tschannen, “Finite scalar quantization: VQ-VAE made simple,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=8ishA3LxN8
2024
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2024
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A. Prasad, K. Lin, J. Wu, L. Zhou, and J. Bohg, “Consistency policy: Accelerated visuomotor policies via consistency distillation,” in Robotics: Science and Systems , 2024
2024
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K. Chen, E. Lim, L. Kelvin, Y. Chen, and H. Soh, “Don’t Start From Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion,” in Proceedings of Robotics: Science and Systems , Delft, Netherlands, July 2024
2024
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A. K. Mondal, S. S. Panigrahi, S. Rajeswar, K. Siddiqi, and S. Ravanbakhsh, “Efficient dynamics modeling in interactive environments with koopman theory,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=fkrYDQaHOJ
2024
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