通过低成本的触觉-力控夹具学习力调节的操控
Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper
AI总结：
本文提出TF-Gripper和RETAF框架，通过低成本触觉-力控夹具和高频率力调节方法，提升机器人在日常力敏感物体上的操控精度与稳定性。
摘要：
成功操控许多日常物体，如薯片，需要精确的力调节。无法调节力会导致任务失败或对物体造成不可逆的损坏。人类可以通过适应触觉反馈来精确实现这一点，即使在短时间内接触物体。我们旨在赋予机器人这种能力。然而，商用夹具表现出高成本或高最小力，使其不适合研究具有日常力敏感物体的力控策略学习。我们介绍了TF-Gripper，一种低成本（约150美元）的力控平行 jaw 夹具，集成了触觉传感作为反馈。它有有效的力范围为0.45-45N，并且兼容不同的机器人手臂。此外，我们设计了一个与TF-Gripper配对的遥控设备来记录人类施加的抓取力。虽然标准低频策略可以在此数据上进行训练，但它们在力调节的反应性和接触依赖性方面表现不佳。为克服这一问题，我们提出了RETAF（REactive Tactile Adaptation of Force），一个将抓取力控制与手臂姿态预测解耦的框架。RETAF通过腕部图像和触觉反馈以高频调节力，同时基础策略预测末端执行器姿态和夹具开合动作。我们评估了TF-Gripper和RETAF在五个需要精确力调节的现实任务中的表现。结果显示，与位置控制相比，直接力控制显著提高了抓取稳定性和任务表现。我们进一步表明触觉反馈对于力调节是必要的，并且RETAF在基准上表现一致，并且可以与各种基础策略集成。我们希望这项工作为在机器人操控中学习力控策略的扩展开辟道路。项目页面：https://force-gripper.github.io
英文摘要：
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 0.45-45 N and is compatible with different robot arms. Additionally, we designed a teleoperation device paired with TF-Gripper to record human-applied grasping forces. While we can train standard low-frequency policies with the collected force data, achieving reliable performance remains challenging due to the reactive and contact-dependent nature of force-regulated manipulation. 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. Our experiments show that, compared to position control, direct force control with TF-Gripper improves grasp stability and overall task performance across six real-world tasks. 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 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
