用无记忆模型+反馈控制，让机器人从轨迹生成精准力控指令
Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
- 无记忆力控生成模型+反馈机制，实现轨迹到力的精准映射
- 在未见过的轨迹上仍能稳定生成力控，实测提升写作任务泛化能力
- 适合需要高精度接触控制的机器人任务，如书写、装配
在接触密集型任务中，位置轨迹通常易获取，但合适的力控指令往往未知。尽管可借助视觉-语言-动作（VLA）等预训练基础模型生成力控指令，但力控高度依赖具体机器人硬件，应用受限。为此，我们提出一种基于位置轨迹的力控生成模型。然而，面对未见过的位置轨迹时，模型难以生成准确力控。为解决此问题，引入反馈控制机制。实验发现，当力控生成模型带有记忆时，反馈控制无法收敛。因此采用无记忆模型，实现稳定反馈。该方法使系统能有效生成未见轨迹对应的力控指令，显著提升真实机器人书写任务的泛化性能。
原文摘要 · Abstract (English)
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.
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