# GIFT: 手套推断力迁移：从可穿戴传感手套到无触觉传感器机器人手的力感知人到机器人技能迁移

> 原文链接：https://robotics.hellobike.com/papers/2026-09-12-2609-14173
> 频道：papers | 更新日期：2026-09-12 | 标签：arxiv, cs.RO
> 核心摘要：GIFT用手套测力与机器人电流残差估力，在杯子抓握保持任务上验证无触觉机器人手的力感知技能迁移。

# GIFT: 手套推断力迁移：从可穿戴传感手套到无触觉传感器机器人手的力感知人到机器人技能迁移

::: v-pre
> 原文链接：[GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors](https://arxiv.org/abs/2609.14173)（arXiv 2609.14173 · 发布日期：2026-09-12）

**GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors**

GIFT用手套测力与机器人电流残差估力，在杯子抓握保持任务上验证无触觉机器人手的力感知技能迁移。

## 核心要点

- 以往手套到机器人技能迁移依赖共享触觉手套或两类触觉传感器对齐。
- 方法在人侧记录手指弯曲、校准指尖力、腕部朝向和头戴相机演示，部署时由电流残差估力。
- 策略使用手套空间状态并预测手指位置目标，机器人仅通过重定向解码器和力估计器接入。
- 杯子抓握保持中两策略各25次均成功，加入力输入使保持阶段估计握力中位数降低53%。

**验证范围与局限**：验证范围为杯子抓握保持任务和观察消融；摘要未说明其他任务或机器人数量。

## 英文摘要 (Abstract)

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.

作者：Tzah Sarusi
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## 原始参考来源 (Sources)

- [arXiv 2609.14173：GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors](https://arxiv.org/abs/2609.14173)