List of papers I have covered:
Liu, Y., Gupta, A., Abbeel, P. and Levine, S., 2018, May. Imitation from observation: Learning to imitate behaviors from raw video via context translation. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1118-1125). IEEE.
Pathak, D., Mahmoudieh, P., Luo, G., Agrawal, P., Chen, D., Shentu, Y., Shelhamer, E., Malik, J., Efros, A.A. and Darrell, T., 2018. Zero-shot visual imitation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 2050-2053).
Torabi, F., Warnell, G. and Stone, P., 2018. Behavioral cloning from observation. arXiv preprint arXiv:1805.01954., IJCAI, 2018 Brown, D., Goo, W., Nagarajan, P. and Niekum, S., 2019, May. Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations. In International conference on machine learning (pp. 783-792). PMLR.
Yang, C., Ma, X., Huang, W., Sun, F., Liu, H., Huang, J. and Gan, C., 2019. Imitation learning from observations by minimizing inverse dynamics disagreement. Advances in neural information processing systems, 32.
Lee, Y., Hu, E.S., Yang, Z. and Lim, J.J., 2019. To follow or not to follow: Selective imitation learning from observations. CoRL, 2019.
Sun, W., Vemula, A., Boots, B. and Bagnell, D., 2019, May. Provably efficient imitation learning from observation alone. In International conference on machine learning (pp. 6036-6045). PMLR.
Torabi, F., Warnell, G. and Stone, P. Generative adversarial imitation from observation. arXiv preprint arXiv:1807.06158., ICML, Workshop, 2019
Edwards, A., Sahni, H., Schroecker, Y. and Isbell, C., 2019, May. Imitating latent policies from observation. In International conference on machine learning (pp. 1755-1763). PMLR.
Liu, F., Ling, Z., Mu, T. and Su, H.. State alignment-based imitation learning. arXiv preprint arXiv:1911.10947., ICLR, 2020
Gangwani, T. and Peng, J., 2020. State-only imitation with transition dynamics mismatch. arXiv preprint arXiv:2002.11879., ICLR, 2020
Zhu, Z., Lin, K., Dai, B. and Zhou, J., 2020. Off-policy imitation learning from observations. Advances in Neural Information Processing Systems, 33, pp.12402-12413.
Kidambi, R., Chang, J. and Sun, W., 2021. Mobile: Model-based imitation learning from observation alone. Advances in Neural Information Processing Systems, 34, pp.28598-28611.
Jaegle, A., Sulsky, Y., Ahuja, A., Bruce, J., Fergus, R. and Wayne, G., 2021, July. Imitation by predicting observations. In International Conference on Machine Learning (pp. 4665-4676). PMLR.
Radosavovic, I., Wang, X., Pinto, L. and Malik, J., 2021, September. State-only imitation learning for dexterous manipulation. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7865-7871). IEEE.
Ma, Y., Shen, A., Jayaraman, D. and Bastani, O., 2022, June. Versatile offline imitation from observations and examples via regularized state-occupancy matching. In International Conference on Machine Learning (pp. 14639-14663). PMLR.
Viano, L., Huang, Y.T., Kamalaruban, P., Innes, C., Ramamoorthy, S. and Weller, A., 2022. Robust learning from observation with model misspecification. arXiv preprint arXiv:2202.06003., AAMAS, 2022
Sonwa, M., Hansen, J. and Belilovsky, E.. Imitation from Observation With Bootstrapped Contrastive Learning. arXiv preprint arXiv:2302.06540., NeurIPS, workshop, 2022
Liu, M., Zhu, Z., Zhuang, Y., Zhang, W., Hao, J., Yu, Y. and Wang, J., 2022. Plan your target and learn your skills: Transferable state-only imitation learning via decoupled policy optimization. arXiv preprint arXiv:2203.02214., ICML, 2022
Chen, S., Ma, X. and Xu, Z., 2022. Imitation learning via differentiable physics. arXiv preprint arXiv:2206.04873. CVPR, 2022
Gangwani, T., Zhou, Y. and Peng, J., 2022. Imitation learning from observations under transition model disparity. arXiv preprint arXiv:2204.11446., ICLR, 2022
Monteiro, J., Gavenski, N., Meneguzzi, F. and Barros, R.C., 2023. Self-Supervised Adversarial Imitation Learning. arXiv preprint arXiv:2304.10914., IJCNN, 2023
Li, C., Blaes, S., Kolev, P., Vlastelica, M., Frey, J. and Martius, G., 2023, May. Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions. In 2023 IEEE international conference on robotics and automation (ICRA) (pp. 2944-2950). IEEE.
Liu, J., He, L., Kang, Y., Zhuang, Z., Wang, D. and Xu, H., 2023. CEIL: Generalized Contextual Imitation Learning. arXiv preprint arXiv:2306.14534., NeuIPS, 2023
Jain, A. and Unhelkar, V.. GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation. arXiv preprint arXiv:2312.10802., AAAI, 2024