# Transfer learning in robotics: An upcoming breakthrough? A review of promises and challenges

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/transfer-learning-in-robotics-an-upcoming-breakthrough-a-review-of-promises-and/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Noémie Jaquier,Michael C. Welle,Andrej Gams,Kunpeng Yao,Bernardo Fichera,Aude Billard,Aleš Ude,Tamim Asfour,Danica Kragic |
| Citations | 37 |
| DOI | 10.1177/02783649241273565 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://journals.sagepub.com/history/e543b361-c7d6-4088-8091-4bbd76ce542e/02783649241273565.17905540.pdf |
| OpenAlex ID | https://openalex.org/W4402545715 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Kunpeng Yao](https://scholariq.org/researchers/kunpeng-yao/)

## Paper primary topic

- [Robot Manipulation and Learning](https://scholariq.org/topics/robot-manipulation-and-learning/)

## Paper topics

- [Robot Manipulation and Learning](https://scholariq.org/topics/robot-manipulation-and-learning/)
- [Domain Adaptation and Few-Shot Learning](https://scholariq.org/topics/domain-adaptation-and-few-shot-learning/)
- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.