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A survey of robot learning from demonstration
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In the citing paper
“-Data Design: Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges [17,23,25-27, 31,33,41,44,74-76,105,114-116].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“-Model Management: Challenge topics on ML model management in the deployment phase, including model validation, decisions on model retraining, adversarial settings, and backwards compatibility of trained models.The governance issues in model management also fit within this category [27,34].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“As mentioned in 4.5, there are challenges on ML model management and governance [18,27,34,65,88].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“Developing the skill set for ML applications is the major challenge related to this KA [15,16,27,32,39,40,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
In the citing paper
“The main challenges in this category are collaboration to ensure a successful project and adequate communication with customers [17,18,21,27,40,59,83].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [27]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
A survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015430 · Cited by 2,855
Zhang, B., Li, C., & Lin, N. (2020) · De Gruyter eBooks · doi:10.1515/9783110595567-011 · Cited by 1,948
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,868
Research works printed but not linked: 5
- T. Inamura, M. Inaba, H. Inoue, Acquisition of probabilistic behavior decision model based on the interactive teaching method, in: Proceedings of the Ninth International Conference on Advanced Robotics, ICAR’99, 1999
- R. Dillmann, M. Kaiser, A. Ude, Acquisition of elementary robot skills from human demonstration, International Symposium on Intelligent Robotic Systems, SIRS’95, 1995
- S. Schaal, D. Sternad, Programmable pattern generators, in: 3rd International Conference on Computational Intelligence in Neuroscience, 1998
- H. Friedrich, R. Dillmann, Robot programming based on a single demonstration and user intentions, in: 3rd European Workshop on Learning Robots at ECML’95, 1995
- M. Kaiser, H. Friedrich, R. Dillmann, Obtaining good performance from a bad teacher, in: Programming by Demonstration vs. Learning from Examples Workshop at ML’95, 1995
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Villani, V., Pini, F., Leali, F., & Secchi, C. (2018) · Mechatronics · doi:10.1016/j.mechatronics.2018.02.009 · Cited by 1,278
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Zhang, T., McCarthy, Z., Jow, O., Lee, D., Chen, X., Goldberg, K., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461249 · Cited by 609
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Wang, L., Gao, R. X., Váncza, J., Krüger, J., Wang, X. V., Makris, S., & Chryssolouris, G. (2019) · CIRP Annals · doi:10.1016/j.cirp.2019.05.002 · Cited by 592
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Mavridis, N. (2015) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.09.031 · Cited by 453
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Kretzschmar, H., Spies, M., Sprunk, C., & Burgard, W. (2016) · The International Journal of Robotics Research · doi:10.1177/0278364915619772 · Cited by 454
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Ross, S., Melik-Barkhudarov, N., Shankar, K. S., Wendel, A., Dey, D., Bagnell, J. A., & Hebert, M. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630809 · Cited by 410
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Muelling, K., Kober, J., Kroemer, O., & Peters, J. (2012) · AAAI Fall Symposium: Robots Learning Interactively from Human Teachers · doi:10.1177/0278364912472380 · Cited by 486
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Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
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Osa, T., Pajarinen, J., Neumann, G. M., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834116 · Cited by 394
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Hasenjäger, M., Heckmann, M., & Wersing, H. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2955910 · Cited by 166
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Brown, D. S., Goo, W., Nagarajan, P., & Niekum, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06387 · Cited by 171
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Moerland, T. M., Broekens, J., & Jonker, C. M. (2018) · Machine Learning · doi:10.1007/s10994-017-5666-0 · Cited by 205
Entity-Centric Coreference Resolution with Model Stacking
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Metadata
- Authors
- Argall, B. D., Chernova, S., Veloso, M., & Browning, B.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Argall, Brenna D.
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 57
- Issue
- 5
- Pages
- 469-483
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W1986014385
- Cited by
- 3,227
- References
- 118
- Citations by year
- 2026: 107 · 2025: 154 · 2024: 152 · 2023: 199 · 2022: 205 · 2021: 286 · 2020: 304 · 2019: 341 · 2018: 256 · 2017: 216 · 2016: 191 · 2015: 204 · 2014: 162 · 2013: 160 · 2012: 117
- FWCI
- 91.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Robotics and Autonomous Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- imitation learning, robot learning, teleoperation, matching function, dynamics model
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