Sim-to-real transfer for contact-rich manipulation remains challenging due to the inherent discrepancy in contact dynamics. While existing methods often rely on costly real-world data or utilize blind compliance through fixed controllers, we propose a framework that leverages expert-designed controller logic for transfer. Inspired by the success of privileged supervision in kinematic tasks, we employ a human-designed finite state machine based position/force controller in simulation to provide privileged guidance. The resulting policy is trained to predict the end-effector pose, contact state, and crucially the desired contact force direction. Unlike force magnitudes, which are highly sensitive to simulation inaccuracies, force directions encode high-level task geometry and remain robust across the sim-to-real gap. At deployment, these predictions configure a force-aware admittance controller. By combining the policy's directional intent with a constant, low-cost manually tuned force magnitude, the system generates adaptive, task-aligned compliance. This tuning is lightweight, typically requiring only a single scalar per contact state. We provide theoretical analysis for stability and robustness to disturbances. Experiments on four real-world tasks, i.e., microwave opening, peg-in-hole, whiteboard wiping, and door opening, demonstrate that our approach significantly outperforms strong baselines in both success rate and robustness. Videos are available at: https://yifei-y.github.io/project-pages/DirectionMatters/.

翻译：由于接触动力学固有的差异，接触式操作的仿真到现实迁移仍然具有挑战性。现有方法通常依赖昂贵的真实世界数据，或通过固定控制器利用盲从顺应性，而我们提出了一个利用专家设计的控制器逻辑进行迁移的框架。受运动学任务中特权监督成功的启发，我们在仿真中使用基于人工设计有限状态机的位置/力控制器来提供特权指导。由此训练出的策略能够预测末端执行器位姿、接触状态，以及至关重要的期望接触力方向。与对仿真误差高度敏感的力大小不同，力方向编码了高层任务几何信息，并在仿真到现实的差距中保持鲁棒性。在部署时，这些预测用于配置一个力感知导纳控制器。通过将策略的方向意图与一个恒定、低成本的手动调整力大小相结合，系统能够生成自适应的、与任务对齐的顺应性。这种调整是轻量级的，通常每个接触状态仅需一个标量。我们提供了关于稳定性和对干扰鲁棒性的理论分析。在四个真实世界任务（即微波炉开门、孔轴装配、白板擦拭和开门）上的实验表明，我们的方法在成功率和鲁棒性方面均显著优于强基线。视频可见于：https://yifei-y.github.io/project-pages/DirectionMatters/。