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| A Self-supervised Learning Method of Target Pushing-Grasping Skills Based on Affordance Map |
| WU Peiliang1,2, LIU Ruijun1, MAO Bingyi1,2, SHI Haoyang1, CHEN Wenbai3, GAO Guowei3 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;
2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China;
3. School of Automation, Beijing Information Science & Technology University, Beijing 100192, China |
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Abstract A self-supervised learning method of target pushing-grasping skills based on affordance map is presented. Firstly, the self-supervised learning problem is described for robot to learn target pushing-grasping skills in cluttered environment. The decision process of robot pushing and grasping operation in workspace is defined as a new Markov decision process (MDP), in which the vision mechanism module and action mechanism module are trained separately. Secondly, the adaptive parameters and group split attention module are fused in the vision mechanism module to design the feature extraction network RGSA-Net, which can generate the affordance map from the original state image of the input network, and provide a good premise for the target pushing-grasping operation. Then, a deep reinforcement learning based self-supervised training framework DQAC based on actor-critic framework is built in the action mechanism module. After the robot performs the action according to the affordance map, the DQAC framework is used to evaluate the action, and thus better cooperation between pushing and grasping is realized. Finally, experimental comparison and analysis are carried out to verify the effectiveness of the proposed method.
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Received: 18 June 2021
Published: 25 July 2022
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