智能调节机械臂刚度，让操作更安全高效。
Stiffness Copilot: An Impedance Policy for Contact-Rich Teleoperation
- 基于视觉实时预测机械臂刚度，动态适应接触任务。
- 实测安全性能媲美低刚度，效率接近高刚度。
- 无需训练即可在真实场景直接使用，适合工业操作。
在高接触性遥操作任务中，选择合适的机器人阻抗至关重要但极具挑战：过柔易损坏环境，过刚则响应迟钝且难以施力。本文提出Stiffness Copilot，一种基于视觉的共享控制策略，操作者控制机器人位姿，系统在线调整阻抗。训练阶段，在仿真中利用特权接触信息推导方向依赖的刚度矩阵；随后，用这些矩阵监督一个轻量级视觉策略，使其从腕部相机图像中预测刚度，并实现零样本迁移至真实图像。人因实验表明，Stiffness Copilot在安全性上达到恒定低刚度水平，同时效率与恒定高刚度相当。
原文摘要 · Abstract (English)
In teleoperation of contact-rich manipulation tasks, selecting robot impedance is critical but difficult. The robot must be compliant to avoid damaging the environment, but stiff to remain responsive and to apply force when needed. In this paper, we present Stiffness Copilot, a vision-based policy for shared-control teleoperation in which the operator commands robot pose and the policy adjusts robot impedance online. To train Stiffness Copilot, we first infer direction-dependent stiffness matrices in simulation using privileged contact information. We then use these matrices to supervise a lightweight vision policy that predicts robot stiffness from wrist-camera images and transfers zero-shot to real images at runtime. In a human-subject study, Stiffness Copilot achieved safety comparable to using a constant low stiffness while matching the efficiency of using a constant high stiffness.
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