Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration-Reference-Cited by-同舟云学术

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Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration

Published:2023-05-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Akbulut Baturhan¹,Girgin Tuba¹,Mehrabi Arash²,Asada Minoru³,Ugur Emre¹,Oztop Erhan²

Affiliation:

1. Bogazici University,Deparment of Computer Engineering,Istanbul,Turkey

2. Ozyegin University,Deparment of Computer Engineering,Istanbul,Turkey

3. Institute for Open and Transdisciplinary, Research Initiatives, Osaka University,Symbiotic Intelligent Systems Research Center,Japan

Funder

Japan Society for the Promotion of Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160895.pdf?arnumber=10160895

Reference31 articles.

1. Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data

2. Tangled: Learning to untangle ropes with RGB-D perception

3. Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

4. Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects

5. Adam: A method for stochastic optimization;kingma;ArXiv Preprint,2014

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1. Coupled Conditional Neural Movement Primitives;Neural Computing and Applications;2024-08-02

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