Deep Reinforcement Learning of Dexterous Pre-Grasp Manipulation for Human-Like Functional Categorical Grasping-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning of Dexterous Pre-Grasp Manipulation for Human-Like Functional Categorical Grasping

Published:2023-08-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
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Author:

Pavlichenko Dmytro¹,Behnke Sven¹

Affiliation:

1. Computer Science Institute VI, University of Bonn,Autonomous Intelligent Systems (AIS) Group,Germany

Funder

German Ministry of Education and Research (BMBF)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10260321/10260291/10260385.pdf?arnumber=10260385

Reference34 articles.

1. Pre-Grasp Sliding Manipulation of Thin Objects Using Soft, Compliant, or Underactuated Hands

2. Randomized physics-based motion planning for grasping in cluttered and uncertain environments;muhayyuddin;IEEE l of Robotics and Automation,2018

3. Real-Robot Deep Reinforcement Learning: Improving Trajectory Tracking of Flexible-Joint Manipulator with Reference Correction

4. Learning dexterous in-hand manipulation

5. Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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