Successfully manipulating many everyday objects, such as potato chips, requires precise force regulation. Failure to modulate force can lead to task failure or irreversible damage to the objects. Humans can precisely achieve this by adapting force from tactile feedback, even within a short period of physical contact. We aim to give robots this capability. However, commercial grippers exhibit high cost or high minimum force, making them unsuitable for studying force-controlled policy learning with everyday force-sensitive objects. We introduce TF-Gripper, a low-cost (~$150) force-controlled parallel-jaw gripper that integrates tactile sensing as feedback. It has an effective force range of 0.45-45N and is compatible with different robot arms. Additionally, we designed a teleoperation device paired with TF-Gripper to record human-applied grasping forces. While standard low-frequency policies can be trained on this data, they struggle with the reactive, contact-dependent nature of force regulation. To overcome this, we propose RETAF (REactive Tactile Adaptation of Force), a framework that decouples grasping force control from arm pose prediction. RETAF regulates force at high frequency using wrist images and tactile feedback, while a base policy predicts end-effector pose and gripper open/close action. We evaluate TF-Gripper and RETAF across five real-world tasks requiring precise force regulation. Results show that compared to position control, direct force control significantly improves grasp stability and task performance. We further show that tactile feedback is essential for force regulation, and that RETAF consistently outperforms baselines and can be integrated with various base policies. We hope this work opens a path for scaling the learning of force-controlled policies in robotic manipulation. Project page: https://force-gripper.github.io .

翻译：成功操作许多日常物体（如薯片）需要精确的力调节。若无法调节力，可能导致任务失败或对物体造成不可逆的损伤。人类能够通过触觉反馈自适应地精确实现这一点，即使在短暂的物理接触期间。我们的目标是赋予机器人这种能力。然而，商用夹爪成本高昂或最小作用力过大，使其不适用于研究日常力敏感物体的力控策略学习。我们介绍了TF-Gripper，一种集成触觉传感作为反馈的低成本（约150美元）力控平行夹爪。其有效力范围为0.45-45N，并能兼容不同的机器人手臂。此外，我们设计了一个与TF-Gripper配对的遥操作设备，用于记录人类施加的抓取力。虽然可以基于这些数据训练标准的低频策略，但它们难以应对力调节的反应性、接触依赖性本质。为克服此问题，我们提出了RETAF（力的反应式触觉适应），一个将抓取力控制与手臂姿态预测解耦的框架。RETAF利用腕部图像和触觉反馈进行高频力调节，而基础策略则预测末端执行器姿态及夹爪开合动作。我们在五个需要精确力调节的真实世界任务中评估了TF-Gripper和RETAF。结果表明，与位置控制相比，直接力控制显著提升了抓取稳定性和任务性能。我们进一步证明触觉反馈对于力调节至关重要，且RETAF持续优于基线方法，并能与多种基础策略集成。我们希望这项工作能为机器人操作中力控策略学习的规模化开辟道路。项目页面：https://force-gripper.github.io。