Toward Orientation Learning and Adaptation in Cartesian Space
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4487
- Type
- D - Journal article
- DOI
-
10.1109/TRO.2020.3010633
- Title of journal
- IEEE Transactions on Robotics
- Article number
- -
- First page
- 82
- Volume
- 37
- Issue
- 1
- ISSN
- 1552-3098
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2020
- 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
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was a joint work of Leeds, Aalto Univ., IDIAP and IIT, which was published in T-RO - the highest impact factor journal in robotics currently.
This paper provided a solution for orientation learning and generalization, where we only need to teach a robot a few demonstrations, and subsequently the robot can apply the learned skills to different scenarios. The main significance is that this can be done without any further human intervention.
A preliminary result of this work was published in ICRA-18, DOI: 10.1109/ICRA.2019.8793540.
A preliminary result of this work was published in: DOI: 10.1109/ICRA.2019.8793540
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
