In contact-rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands using a pretrained foundation model such as Vision-Language-Action (VLA) models, force control is highly dependent on the specific hardware of the robot, which makes the application of such models challenging. To bridge this gap, we propose a force generative model that estimates force commands from given position trajectories. However, when dealing with unseen position trajectories, the model struggles to generate accurate force commands. To address this, we introduce a feedback control mechanism. Our experiments reveal that feedback control does not converge when the force generative model has memory. We therefore adopt a model without memory, enabling stable feedback control. This approach allows the system to generate force commands effectively, even for unseen position trajectories, improving generalization for real-world robot writing tasks.

翻译：在接触密集型任务中，位置轨迹通常易于获取，但合适的力指令往往未知。尽管可以设想使用预训练的基础模型（如视觉-语言-动作模型）生成力指令，但力控制高度依赖于机器人的具体硬件，这使得此类模型的应用面临挑战。为弥合这一差距，我们提出了一种力生成模型，能够从给定的位置轨迹估计出力指令。然而，当处理未见过的位置轨迹时，该模型难以生成准确的力指令。为解决这一问题，我们引入了反馈控制机制。实验表明，当力生成模型具有记忆性时，反馈控制无法收敛。因此，我们采用无记忆模型，实现了稳定的反馈控制。该方法使系统能够有效生成力指令，即使对于未见过的位置轨迹也能适用，从而提升了真实机器人书写任务的泛化能力。