Kernelized movement primitives
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4088
- Type
- D - Journal article
- DOI
-
10.1177/0278364919846363
- Title of journal
- The International Journal of Robotics Research
- Article number
- -
- First page
- 833
- Volume
- 38
- Issue
- 7
- ISSN
- 0278-3649
- Open access status
- Deposit exception
- Month of publication
- May
- Year of publication
- 2019
- 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
-
3
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 18
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper is that it presented the first framework which can solve most of the key issues arising from imitation learning.
This work has led to three extension papers: DOI: 10.1109/ICRA40945.2020.9196821, DOI: 10.1109/IROS40897.2019.8967996, “Robust gait synthesis combining constrained optimization and imitation learning” (IROS2020, accepted)
This work was also employed by other researchers: DOI: 10.1109/LRA.2020.3006818
A granted China CSC Scholarship for PhD students in 2020 is building on this work.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
