WU Peiliang, LIU Ruijun, MAO Bingyi, SHI Haoyang, CHEN Wenbai, GAO Guowei. A Self-supervised Learning Method of Target Pushing-Grasping Skills Based on Affordance Map[J]. ROBOT, 2022, 44(4): 385-398. DOI: 10.13973/j.cnki.robot.210265
Citation: WU Peiliang, LIU Ruijun, MAO Bingyi, SHI Haoyang, CHEN Wenbai, GAO Guowei. A Self-supervised Learning Method of Target Pushing-Grasping Skills Based on Affordance Map[J]. ROBOT, 2022, 44(4): 385-398. DOI: 10.13973/j.cnki.robot.210265

A Self-supervised Learning Method of Target Pushing-Grasping Skills Based on Affordance Map

  • 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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