Predicting grasp success in the real world - A study of quality metrics and human assessment

Author:

Rubert Carlos,Kappler Daniel,Bohg Jeannette,Morales Antonio

Funder

Ministerio de Educación, Ciencia y Tecnología, Spain

Ministerio de Ciencia e Innovación, Spain

Ministerio de Economía y Competitividad, Spain

Generalitat Valenciana, Spain

Fundació Caixa Castelló-Bancaixa, Spain

Publisher

Elsevier BV

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering

Reference45 articles.

1. Robot grasp synthesis algorithms: A survey;Shimoga;Int. J. Robot. Res.,1996

2. Grasp quality measures: review and performance;Roa;Auton. Robots,2014

3. Data-driven grasps synthesis - A survey;Bohg;IEEE Trans. Robot.,2014

4. Leveraging big data for grasp planning;Kappler,2015

5. Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards;Mahler,2016

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