感受力：从人类接触驱动的学习
Feel the Force: Contact-Driven Learning from Humans
AI总结：
本文提出FeelTheForce系统，通过触觉手套采集人类接触力并结合视觉手部姿态估计训练闭环策略，将其迁移到Franka Panda机器人上实现力敏感操作，在5项任务中取得77%成功率。
摘要：
在操作过程中控制细粒度力仍然是机器人领域的核心挑战。虽然从机器人采集的数据或仿真中学习的机器人策略显示出前景，但它们难以泛化到多样化的真实世界交互中。直接从人类学习提供了一种可扩展的解决方案，使演示者能够以其自然形态和日常环境中执行技能。然而，仅凭视觉演示缺乏推断精确接触力所需的信息。我们提出FeelTheForce（FTF）：一个机器人学习系统，通过建模人类触觉行为来学习力敏感操作。使用触觉手套测量接触力，并使用基于视觉的模型估计手部姿态，我们训练了一个闭环策略，该策略持续预测操作所需的力。该策略通过共享的视觉和动作表示重新定向到配备触觉夹爪传感器的Franka Panda机器人。在执行时，PD控制器调节夹爪闭合以跟踪预测力，从而实现精确的力感知控制。我们的方法将鲁棒的低层力控制建立在可扩展的人类监督之上，在5个力敏感操作任务中取得了77%的成功率。代码和视频可在https://feel-the-force-ftf.github.io获取。
英文摘要：
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contact forces. We present FeelTheForce (FTF): a robot learning system that models human tactile behavior to learn force-sensitive manipulation. Using a tactile glove to measure contact forces and a vision-based model to estimate hand pose, we train a closed-loop policy that continuously predicts the forces needed for manipulation. This policy is re-targeted to a Franka Panda robot with tactile gripper sensors using shared visual and action representations. At execution, a PD controller modulates gripper closure to track predicted forces-enabling precise, force-aware control. Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks. Code and videos are available at https://feel-the-force-ftf.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。
