# Sim-to-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/sim-to-real-transfer-for-visual-reinforcement-learning-of-deformable-object/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Paul Maria Scheikl,Eleonora Tagliabue,Balázs Gyenes,Martin Wagner,Diego Dall’Alba,Paolo Fiorini,Franziska Mathis-Ullrich |
| Citations | 69 |
| DOI | 10.1109/lra.2022.3227873 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2406.06092 |
| OpenAlex ID | https://openalex.org/W4312578227 |
| Type | article |
| Year | 2022 |

## Paper authors

- [Franziska Mathis-Ullrich](https://scholariq.org/researchers/franziska-mathis-ullrich/)

## Paper journal

- [IEEE Robotics and Automation Letters](https://scholariq.org/journals/ieee-robotics-and-automation-letters/)

## Paper primary topic

- [Multimodal Machine Learning Applications](https://scholariq.org/topics/multimodal-machine-learning-applications/)

## Paper topics

- [Multimodal Machine Learning Applications](https://scholariq.org/topics/multimodal-machine-learning-applications/)
- [Advanced Vision and Imaging](https://scholariq.org/topics/advanced-vision-and-imaging/)
- [Soft Robotics and Applications](https://scholariq.org/topics/soft-robotics-and-applications/)

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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.