The DOI matches its Crossref record; 114 of the 118 references it cites were found in a registry or an index.
A survey of robot learning from demonstration
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Flagged first, then by citations
4 of the 118 references couldn’t be matched to a known paper.
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
Cited by
Sorted by citations
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Villani, V., Pini, F., Leali, F., & Secchi, C. (2018) · Mechatronics · doi:10.1016/j.mechatronics.2018.02.009 · Cited by 1,278
Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015) · Nature · doi:10.1038/nature14422 · Cited by 979
Imitation Learning: A Survey of Learning Methods
Hussein, A., Gaber, M. M., Elyan, E., & Jayne, C. (2018) · ACM Computing Surveys · doi:10.1145/3054912 · Cited by 1,060
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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