Abstract
The robot’s weak ability to manipulate deformable objects makes robots rarely used in the garment manufacturing industry. Designing a robot skill acquisition frame that can learn to manipulate fabrics helps to improve the intelligence of the garment manufacturing industry. This paper proposes a process learning framework for robot fabric stacking based on a composite reward function. A limited task flow model describes the overall process, and robot skills represent a single task. Based on the robot’s acquisition of operational skills, a priori knowledge of the technological process is embedded into the reward function to form a composite reward function, and then it takes to guide the robot to use the acquired skills to complete the overall process task. Experiments are conducted on the UR5e robot to prove the effectiveness of this method, and results show that the robot guided by the composite reward function can complete the process task of fabric manipulation before garment sewing.
Supported by Shandong Major Science and Technology Innovation Project(No.2019JZZY010430), Shandong Major Science and Technology Innovation Project(No.2019JZZY010429), Shandong Provincial Key Research and Development Program(Grand No.2019TSLH0302).
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Fu, T., Li, F., Zheng, Y., Song, R. (2021). Process Learning of Robot Fabric Manipulation Based on Composite Reward Functions. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_16
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