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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]
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Self-citation
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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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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