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- W4285102571 abstract "Kinematics and instantaneous kinematics are fundamental in many robotic tasks, such as positioning and collision avoidance. Existing learning methods mainly concern a single robot, and small-scale networks are sufficient for considerable approximation accuracy. A question is: Can we learn a kinematics model that can generalize to various robots rather than a single robot? This paper studies the kinematics learning of massive heterogeneous serial robots and the transfer of these general models to reality. We generate a dataset by randomizing dimensions, configurations, and link lengths and employ a network based on the generative pre-trained transformer to learn general kinematics mappings. We directly transfer our models for accuracy and use distillation-based transfer for computational efficiency. The results validate that our method can accurately approximate the kinematics of thousands of robot models and demonstrates generality in transfer." @default.
- W4285102571 created "2022-07-14" @default.
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- W4285102571 date "2022-05-23" @default.
- W4285102571 modified "2023-09-29" @default.
- W4285102571 title "Kinematics Learning of Massive Heterogeneous Serial Robots" @default.
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- W4285102571 doi "https://doi.org/10.1109/icra46639.2022.9812021" @default.
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