Compliant control of robots in dynamic uncertain environments

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The proposed method improves constant force tracking during massage therapy with rehabilitation robots.
Simulation experiments validated the method's effectiveness in optimizing learning efficiency and control stability.
PID feedforward compensation helps mitigate errors caused by environmental uncertainties during operation.
Deep reinforcement learning is utilized for superior decision-making, enabling robust control in complex scenarios.

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This study demonstrates enhanced robot adaptability and stability using the SAC algorithm and PID compensation.

Wang et al. (2025) studied this question.

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