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Robotics机器人Published · Humanoids & IROS 2025已发表 · Humanoids、IROS 2025

Human-inspired robotic texture exploration触摸速度和动作怎样影响纹理识别

How do touch speed and exploratory movements change what people and robots learn about texture?触摸速度和探索动作,会怎样改变人和机器人对纹理的感知?

Studies velocity modulation and compound exploratory motions to improve texture discrimination under realistic contact variation.我通过人类触摸实验和神经形态机器人实验,分析速度与复合动作对纹理识别的影响。

Role项目角色
First author · Equal-contribution researcher第一作者 · 共同一作
Period项目时间
2022–present
Active touch主动触觉Texture discrimination纹理识别Human perception人的触觉感知
MODEL / ACTIVE TOUCH模型 / 主动触觉Concept model概念图
87.33%Best texture accuracy最高纹理识别率
8.04 mWReported power论文报告的功耗
R² 0.77Human-speed model偏好触摸速度模型

01 / problem问题

Why movement matters研究问题

Texture is not sensed passively: velocity, force and exploratory motion change the tactile evidence available to a person or robot.触摸速度、接触力和探索动作会改变人或机器人采集到的纹理信息。

02 / contribution工作

My contribution我负责的工作

I first-authored the human velocity-modulation study and contributed equally to the neuromorphic robotic movement study.我以第一作者完成了人类速度调节研究,并以共同一作身份参与神经形态机器人的探索动作研究。

03 / approach方法

Human-to-robot method实验设计

A human study tested 10 natural textures with 40 participants across six velocities and four force conditions. The robotic study compared six distinct or combined exploratory motions.人类实验招募 40 名参与者,在 6 种速度和 4 种接触力下探索 10 种自然纹理。机器人实验比较 6 种单独或组合的探索动作。

  • Public acquisition code and research materials for the human single-finger exploration experiments are available on GitHub.人类单指探索实验的采集代码与研究材料已公开在 GitHub。

04 / outcome结果

Measured outcomes实验结果

Human perceptual dimensions predicted preferred speed with R²=0.77. Sliding plus rotating reached 87.33% texture accuracy at a reported 8.04 mW.人类感知维度对偏好触摸速度的预测达到 R²=0.77。机器人采用滑动加旋转的组合动作时,纹理识别率达到 87.33%,论文报告的功耗为 8.04 mW。

Evidence论文与材料

Read the source material查看论文与项目材料