The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
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“GUAPO [10] also decomposes object manipulation into a coarse alignment phase, approaching the object by feeding visual observations into a neural network, and a last-inch learned policy is used to perform the interaction.”
From On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation (2024) · cites this paper as [10]
In the citing paper
“This method lacks an explicit retrieval phase, as the affordances are recognised implicitly by a neural network.”
From On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation (2024) · cites this paper as [10]
In the citing paper
“Different methods in the recent literature have proposed augmenting robot manipulation with retrieval or decomposition of the trajectory, similar to the approach we adopt [10,18,2,12].”
From On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation (2024) · cites this paper as [10]
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Citations
Cites
Overcoming Exploration in Reinforcement Learning with Demonstrations
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8463162 · Cited by 712
Silver, T., Allen, K., Tenenbaum, J., & Kaelbling, L. (2018) · arXiv · doi:10.48550/arxiv.1812.06298 · Cited by 113
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Cited by
Sorted by citations
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks
Nasiriany, S., Liu, H., & Zhu, Y. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9812140 · Cited by 91
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Sundaresan*, P., Grannen*, J., Thananjeyan, B., Balakrishna, A., Ichnowski, J., Novoseller, E., Hwang, M., Laskey, M., Gonzalez, J., & Goldberg, K. (2021) · Robotics: Science and Systems XVII · doi:10.15607/rss.2021.xvii.013 · Cited by 26
On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation
Palo, N. D., & Johns, E. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3349832 · Cited by 14
“As the original paper [10] proposes the use of Reinforcement Learning for the policy, while we use Behaviour Cloning for time efficiency, we denote this method as BC-GUAPO.”
BackgroundMethodInfluential
Metadata
- Authors
- Lee, M. A., Florensa, C., Tremblay, J., Ratliff, N., Garg, A., Ramos, F., & Fox, D.
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 7505-7512
- Cited by
- 46
- References
- 2
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