GIFT：手套推断力传递——从可穿戴传感手套到无触觉传感器机器人手的力感知人机技能迁移
GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors
AI总结：
GIFT提出一种无需共享触觉传感器的人机技能迁移方法，通过手套测力与机器人电流估计力，实现力感知抓取，实验证明力输入显著降低抓取力。
AI中文摘要：
迄今为止，从传感手套进行的人到机器人技能迁移依赖于共享硬件：演示者和机器人佩戴相同的触觉手套，或者学习两个触觉传感器之间的对齐。我们提出GIFT（手套推断力传递），这是一种流水线，其中人与机器人之间的接口是物理单元而非共享传感器：指尖力在人类侧以牛顿为单位测量，在机器人侧以牛顿为单位估计。可穿戴手套记录手指弯曲、校准的指尖力和手腕方向，而头戴式摄像头记录演示过程；不需要机器人存在。在部署时，机器人通过相对于自由空间基线的执行器电流残差来估计力，并通过校准映射转换为牛顿，因此任何报告电机电流的位置控制手都可以作为部署平台。策略使用手套空间状态并预测手指位置目标；机器人仅通过两个校准适配器进入，即重定向解码器和力估计器。我们在一个杯子抓取和保持任务上评估GIFT，使用两个在相同演示上训练的动作分块策略，其中一个保留指尖力输入，另一个将指尖力输入置零。在50次 rollout 的评估中，样本量和指标在评分前固定，两个策略在所有25次 rollout 中均成功。带力输入的每次 rollout 保持阶段抓取力估计的中位数低53%：1.20 N对比2.55 N（单侧 Mann-Whitney U，p<0.0001）。在观察消融中，仅视觉策略实现了0/15次抓取，给予手命令状态的策略获得了抓取，而力输入决定了策略保持的力度。因此，在人类手上测量的力通道通过从五个手套通道到七个机器人执行器的重定向映射，转移到没有触觉硬件的机器人手上，两者之间没有共享传感器。
英文摘要：
Human-to-robot skill transfer from sensing gloves has so far relied on shared hardware: the same tactile glove worn by the demonstrator and the robot, or a learned alignment between two tactile sensors. We present GIFT (Glove-Inferred Force Transfer), a pipeline in which the interface between human and robot is a physical unit rather than a shared sensor: fingertip force is measured in newtons on the human side and estimated in newtons on the robot side. A wearable glove records finger flexion, calibrated fingertip force, and wrist orientation, while a head-mounted camera records the demonstration; no robot is present. At deployment, the robot estimates force from actuator-current residuals relative to a free-space baseline, through a calibrated mapping to newtons, so any position-controlled hand that reports motor current can serve as the deployment platform. The policy uses a glove-space state and predicts finger-position targets; the robot enters only through two calibrated adapters, a retargeting decoder and a force estimator. We evaluate GIFT on a cup grasp-and-hold task with two action-chunking policies trained on the same demonstrations, with fingertip-force inputs retained in one and zeroed in the other. In a 50-rollout evaluation with sample size and metrics fixed before scoring, both policies succeeded in all 25 rollouts. The median of the per-rollout hold-phase grip-force estimates was 53% lower with force inputs: 1.20 N versus 2.55 N (one-sided Mann-Whitney U, p<0.0001). In an observation ablation, a vision-only policy achieved 0/15 grasps, policies given hand-command state acquired the grasp, and the force inputs determined how hard the policy held. A force channel measured on the human hand thus transfers to a robot hand with no tactile hardware, through a retargeting map from five glove channels to seven robot actuators, with no sensor shared between the two.
