A survey of robot learning from demonstration
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Design issues on data collection, pre-processing, cleaning, labeling and augmentation, including big data challenges, exist for ML applications.
“-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]
Model management in the deployment phase, including model validation, decisions on model retraining, adversarial settings, backwards compatibility of trained models, and governance issues, is a maintenance challenge.
“-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]
There are challenges on ML model management and governance.
“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]
Developing the skill set for ML applications is the major challenge related to the software engineering professional practice knowledge area.
“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]
The main challenges in group dynamics and psychology are collaboration to ensure a successful project and adequate communication with customers.
“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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- 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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