Reinforcement learning and sim-to-real transfer have made significant progress in dexterous manipulation. However, progress remains limited by the difficulty of simulating complex contact dynamics and multisensory signals, especially tactile feedback. In this work, we propose \ours, a sim-to-real framework that addresses these limitations and demonstrates its effectiveness on nut-bolt fastening and screwdriving with multi-fingered hands. The framework has three stages. First, we train reinforcement learning policies in simulation using simplified object models that lead to the emergence of correct finger gaits. We then use the learned policy as a skill primitive within a teleoperation system to collect real-world demonstrations that contain tactile and proprioceptive information. Finally, we train a behavior cloning policy that incorporates tactile sensing and show that it generalizes to nuts and screwdrivers with diverse geometries. Experiments across both tasks show high task progress ratios compared to direct sim-to-real transfer and robust performance even on unseen object shapes and under external perturbations. Videos and code are available on https://dexscrew.github.io.

翻译：强化学习与仿真到现实迁移在灵巧操作领域已取得显著进展。然而，复杂接触动力学与多感官信号（尤其是触觉反馈）的仿真难度仍制约着进一步发展。本研究提出 \ours 框架，该仿真到现实框架解决了上述局限，并在多指手执行螺母螺栓紧固与螺钉旋拧任务中验证了其有效性。该框架包含三个阶段：首先，我们使用简化的物体模型在仿真环境中训练强化学习策略，促使正确指步态的形成；随后，将习得策略作为技能基元集成至遥操作系统，采集包含触觉与本体感觉信息的真实世界演示数据；最后，训练融合触觉感知的行为克隆策略，并证明该策略能够泛化至不同几何形状的螺母与螺丝刀。两项任务的实验表明：相较于直接仿真到现实迁移，本方法实现了更高的任务完成率，即使面对未见过的物体形状及外部干扰仍保持鲁棒性能。演示视频与代码发布于 https://dexscrew.github.io。