Robots that can adapt like animals
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24790883
- Type
- D - Journal article
- DOI
-
10.1038/nature14422
- Title of journal
- Nature
- Article number
- -
- First page
- 503
- Volume
- 521
- Issue
- 7553
- ISSN
- 0028-0836
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
https://static-content.springer.com/esm/art%3A10.1038%2Fnature14422/MediaObjects/41586_2015_BFnature14422_MOESM64_ESM.pdf
- 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)
-
-
- Citation count
- 299
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper develops algorithms that allow robots to recover from sustained damages in no more than two minutes – an order of magnitude faster than state-of-art approaches. The publication of this result was featured on the front cover of Nature. The research attracted over 40,000 views on Nature’s website. It was covered by around 40 news outlets including BBC News, Washington Post, The Guardian, The New York Times, and BBC Radio (see Nature online attention metrics at https://www.nature.com/articles/nature14422/metrics); The accompanying videos demonstrating robot damage recovery received over 200k views on YouTube (goo.gl/6zGaXv).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -