Fetching the paper…
Reading the bibliography…
Reinforcement learning and Imitation Learning approaches utilize policy learning strategies that are difficult to generalize well with just a few examples of a task.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011), ‘Scikit-learn: Machine learning in Python’, Journal of Machine Learning Research
2011
Earlier work this paper cites.
Hussein, A., Gaber, M. M., Elyan, E. & Jayne, C. (2017), ‘Imitation learning: A survey of learning methods’, ACM Computing Surveys (CSUR)
2017
Earlier work this paper cites.
Schilling, M. & Melnik, A. (2019), An approach to hierarchical deep reinforcement learning for a decentralized walking control architecture, in
2018
Earlier work this paper cites.
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019), ‘Bert: Pre-training of deep bidirectional transformers for language understanding’
2019
Earlier work this paper cites.
Nguyen, H. & La, H. (2019), Review of deep reinforcement learning for robot manipulation, in
2019
Earlier work this paper cites.
Bach, N., Melnik, A., Schilling, M., Korthals, T. & Ritter, H. (2020), Learn to move through a combination of policy gradient algorithms: Ddpg, d4pg, and td3, in
2020
Earlier work this paper cites.
Lynch, C. & Sermanet, P. (2021), ‘Language conditioned imitation learning over unstructured data’
2021
Earlier work this paper cites.
Melnik, A., Harter, A., Limberg, C., Rana, K., Sünderhauf, N. & Ritter, H. (2021), Critic guided segmentation of rewarding objects in first-person views, in
2021
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Li, L. H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.-N., Chang, K.-W. & Gao, J. (2022), ‘Grounded language-image pre-training’
2022
Cited alongside, same era.
Mees, O., Hermann, L., Rosete-Beas, E. & Burgard, W. (2022), ‘Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks’, IEEE Robotics and Automation Letters (RA-L)
2022
Later among the works it cites.
Li, Z., Zhang, X., Zhang, Y., Long, D., Xie, P. & Zhang, M. (2023), ‘Towards general text embeddings with multi-stage contrastive learning’
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
Mees, O., Hermann, L. & Burgard, W. (2022), ‘What matters in language conditioned robotic imitation learning over unstructured data’, IEEE Robotics and Automation Letters
2022
Cited alongside, same era.
2023
Closest in time.
Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M. & Wang, J. (2023), ‘Fast segment anything’
2023
Closest in time.