用视觉预测力：无触觉传感器的柔顺抓取方法
Robot Learning with Visual Predicted Force
夹爪形变就能估力，摘莓、插插头这类精细活儿不用装触觉传感器了，做机器人操作的同学可以看看这篇论文的做法。
一篇 arXiv 论文提出不依赖力/触觉传感器，改用 Fin Ray 柔顺夹爪的形变做视觉力预测。方法分两步：先用标定数据训练视觉力估计器，为示教数据标注力估计值；再在这些力增广数据上训练动作-力提案策略，联合生成候选动作及其预期力。测试时采样候选动作，执行预测力最接近示教目标的那个。在摘莓、空罐抓取、手内重定向和插头插入四类任务上验证有效，部署时不需要任何力或触觉传感器。
Robot Learning with Visual Predicted Force
Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through visual force prediction from the deformation of a compliant Fin Ray gripper. Our approach trains two models. First, we train a visual force estimator on calibration data and use it to annotate task demonstrations with force estimates. Second, we train an action--force proposal policy on these force-augmented demonstrations to jointly generate candidate robot actions and their associated forces. At test time, we sample candidate actions and the forces they are expected to produce, then execute the action whose predicted force is closest to a target from the demonstrations. We evaluate our approach on berry picking, empty-can grasping, in-hand reorientation, and plug insertion. Our results show that visual force prediction can guide inference-time action selection for contact-rich manipulation without requiring force or tactile sensors at deployment.
