# End-to-End Learning of Object Grasp Poses in the Amazon Robotics Challenge

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/end-to-end-learning-of-object-grasp-poses-in-the-amazon-robotics-challenge/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Eiichi Matsumoto,Masaki Saito,Ayaka Kume,Jethro Tan |
| Citations | 20 |
| DOI | 10.1007/978-3-030-35679-8_6 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3022468211 |
| Type | book-chapter |
| Year | 2020 |

## Paper authors

- [Masaki Saito](https://scholariq.org/researchers/masaki-saito/)

## 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/)
- [Soft Robotics and Applications](https://scholariq.org/topics/soft-robotics-and-applications/)
- [Robotic Path Planning Algorithms](https://scholariq.org/topics/robotic-path-planning-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.