Variable-Damping Impedance Control for Contact Tasks: A Reinforcement Learning Method Integrating HER and Importance Sampling.

Force control is crucial for robotic contact-rich tasks such as assembly, grinding, and polishing, directly affecting task accuracy, interaction stability, and safety. Yet in unstructured environments, environmental uncertainty, contact oscillations, and friction disturbances make high-performance contact control and reinforcement learning policy optimization difficult. To address this issue, this paper proposes a deep reinforcement learning-based variable-damping impedance control method that integrates parameterized Hindsight Experience Replay (TO-HER) and Importance Sampling (IS). Within the impedance control framework, a residual parameterized policy enables online damping adjustment, improving dynamic adaptability across contact phases. To overcome the training instability of conventional HER in contact-intensive tasks, a parameterized goal relabeling mechanism is introduced to improve relabeled sample quality and sample efficiency. In addition, an importance sampling scheme based on density ratio estimation mitigates the distribution mismatch between relabeled and real samples, enhancing training quality and stability. Experimental results show that the proposed method outperforms baseline methods in convergence, final return, and training stability, while achieving higher tracking accuracy and better dynamic response in typical contact scenarios, demonstrating strong effectiveness and robustness for robotic contact control in unstructured environments.