most citedLearning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study

8 citations · 8 across the 1 of their papers we have counts for

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1 paper

cs.RO2022★ 8 cited

Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study

Hongbin Lin, Qian Gao, Xiangyu Chu +4

Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown syste…