Abstract
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: this https URL .
Abstract (translated)
成功操控许多日常物品，如薯片等，需要精确的力量调节。未能适当调整力量可能会导致任务失败或对物体造成不可逆的损害。人类可以通过触觉反馈来适应这种力的变化，并且即使在短暂的身体接触中也能实现这一点。我们的目标是赋予机器人这项能力。然而，市面上的机械手爪表现出高昂的成本或是最小作用力过大，这使得它们不适合用于研究与日常敏感物品相关的力控制策略学习。 我们引入了TF-Gripper，这是一个低成本（约150美元）且能够集成触觉感应反馈的力控平行夹爪。它具有0.45-45牛的有效力范围，并且可以兼容不同型号的机械臂。此外，我们还设计了一种与TF-Gripper配套使用的远程操作设备，用于记录人类施加的抓握力量。虽然标准的低频策略可以在这些数据上进行训练，但它们难以应对这种依赖接触反应性的力调节特性。 为了解决这个问题，我们提出了RETAF（REactive Tactile Adaptation of Force），这是一个将抓取力控制与机械臂姿态预测解耦的框架。RETAF通过手腕图像和触觉反馈在高频下调节力量，而基础策略则用于预测末端执行器的姿态以及夹爪开/关动作。 我们在五个需要精确力量调节的真实世界任务中评估了TF-Gripper和RETAF的表现。结果显示，与位置控制相比，直接力控显著提高了抓取稳定性及任务性能。我们进一步证明触觉反馈对力量调节至关重要，并且RETAF在各种基础策略下始终优于基线表现并可进行集成。 希望这项工作能为机器人操作中学习力控策略的规模化应用开辟一条新路径。 项目页面: 这里（请将“this https URL”替换为您实际提供的链接）。
URL
https://arxiv.org/abs/2602.10013
