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Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning
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DOI：10.1109/TII.2023.3242831.png)
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摘要
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This article proposes a platform for robots to learn disassembly tasks based on reinforcement learning (RL) techniques. The platform is demonstrated by a robot learning the skill of removing a bolt along a door-chain groove in a data-driven way, where the clearance between the bolt and the groove is less than 1 mm. Furthermore, the relationship between the performance of the learned skills and the precision of the robot is studied with a method to control the robot's precision by adding uncorrelated zero-mean Gaussian noise to the robot's actions. Finally, the transferability of the learned skills among robots with different precisions is empirically studied. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision.
Keyword:
Robots
Task analysis
Robot kinematics
Training
Service robots
Reinforcement learning
Fasteners
Machine learning
reinforcement learning (RL)
remanufacturing
robotic disassembly
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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