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This paper proposes a new robot needle insertion trajectory planning method based on learning expert's skill. Through reforcement learning, the system can imitate the expert's behavior in planning optimal needle insertion policy. After learning two experts' skill and experience, the needle insertion optimal policy shows that each one can catch the main(More)
Since the gravity terms depend only on the link positions in compliant joint robots, a neural-network-based gravity compensation scheme is conceived while the gravity model is unknown or is too complicated to be expressed explicitly. A PD-type control with this compensation is developed with the high-gain torque inner loop such that singular perturbation(More)
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