Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning

Author:

Serhan Baris1,Pandya Harit2,Kucukyilmaz Ayse1,Neumann Gerhard3

Affiliation:

1. School of Computer Science, University of Nottingham,UK

2. Toshiba Research,Cambridge,UK

3. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology,Germany

Funder

NCNR (National Centre for Nuclear Robotics, EPSRC)

Publisher

IEEE

Cited by 5 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. A Multitask and Kernel Approach for Learning to Push Objects with a Target-Parameterized Deep Q-Network;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

2. TRF-Net: a transformer-based RGB-D fusion network for desktop object instance segmentation;Neural Computing and Applications;2023-08-05

3. Constructing a Sorting Robot for Densely Gathered Mixed Industrial Waste Items;Journal of the Robotics Society of Japan;2023

4. RL-Studio: A Tool for Reinforcement Learning Methods in Robotics;ROBOT2022: Fifth Iberian Robotics Conference;2022-11-19

5. Review of Learning-Based Robotic Manipulation in Cluttered Environments;Sensors;2022-10-18

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