让机器人同时预测动作和控制模式，实现更稳定的接触操作。
URF: A Unified Robot Control-Policy Framework for Stable Contact Aware Manipulation
- 统一框架预测虚拟目标、刚度矩阵和控制切换比例。
- 在翻箱和压线任务中成功率更高，故障率降低。
- 适合需要稳定接触的工业抓取与装配场景。
基于学习的操控策略通常从感知观测中预测机器人动作，并交由独立的低层控制器执行。在刚性接触场景下，这种分离会导致问题：相同的虚拟目标或柔顺动作指令可能引发不稳定接触、跟踪误差、过载或工具损坏，具体取决于底层控制器。本文提出一种统一机器人控制-策略框架（URF），将柔顺动作预测与统一阻抗-导纳控制相结合。给定多模态观测，URF同时预测虚拟目标、刚度矩阵和阻抗-导纳切换比例。该比例决定控制器何时以导纳模式主导以实现精准运动追踪，何时转为阻抗模式以保障刚性接触安全。由于示范数据缺乏环境刚度真值，我们通过测量接触力构建切换比例标签，用于监督控制器模式预测。在翻箱与压线任务中，URF显著提升任务成功率，减少仅使用导纳控制时出现的快速力上升、大幅力振荡、工具断裂及机器人安全停机等失败模式。结果表明，接触感知策略不仅需预测柔顺动作，还需预测执行所用的控制器行为。
原文摘要 · Abstract (English)
Learning-based manipulation policies usually predict robot actions from sensory observations and leave their execution to a separate low-level controller. In rigid contact, this separation can be problematic: the same motion to a virtual target or compliant motion command can lead to unstable contact, tracking error, excessive loading, or tool damage, depending on the low-level controller. In this paper, we propose a \textit{Unified Robot Control-Policy Framework} (URF), which connects compliant action prediction with unified impedance-admittance control. Given multimodal observations, URF predicts a virtual target, a stiffness matrix, and an impedance-admittance switch ratio. The switch ratio determines when the controller should behave more like admittance control for accurate motion tracking and when it should move toward impedance control for safer rigid contact. Because demonstration data do not provide ground-truth environment stiffness, we construct switch-ratio labels from measured contact forces and use them to supervise controller-mode prediction. Across box-flipping and line-pressing tasks, URF achieves higher task success rates while reducing failure modes observed with admittance-only execution, including rapid force buildup, large force oscillations, tool breakage, and robot safety stops. These results suggest that contact-aware policies benefit from predicting not only compliant actions but also the controller behavior used to execute them. Project page: https://jiyou384.github.io/urf_project_page/
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