Biologically-inspired motion modeling and neural control for robot learning from demonstrations
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14575292
- Type
- D - Journal article
- DOI
-
10.1109/TCDS.2018.2866477
- Title of journal
- IEEE Transactions on Cognitive and Developmental Systems
- Article number
- 0
- First page
- 281
- Volume
- 11
- Issue
- 2
- ISSN
- 2379-8920
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
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B - Computational Intelligence
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a novel framework for robot learning from a demonstration. It considers the performance of both a motion model and a dynamics controller to cognize the dynamic environment and to compensate for the unknown dynamics. The proposed methods have been successfully implemented into the Baxter robot to learn daily motion skills from a human demonstration.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -