Efficient exploratory learning of inverse kinematics on a bionic elephant trunk
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 184805060
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2013.2287890
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 1147
- Volume
- 25
- Issue
- 6
- ISSN
- 2162-237X
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- 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
-
1
- Research group(s)
-
-
- Citation count
- 69
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article is the crowning achievement of a highly original research project starting off biological inspiration to develop an original machine learning model, eventually solving a hard control problem on a robotic system with industrial relevance. The paper did achieve the first ever kinematic controller of this robot, before even the manufacturer could develop one, and still to date has the best published accuracy on it. The work is very well cited and gained popular impact through coverage in New Scientist and IEEE Spectrum.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -