计算机科学
机器人学
扭矩
机器人
人工智能
触觉传感器
钥匙(锁)
计算机视觉
切片
对象(语法)
运动(物理)
工作(物理)
航程(航空)
机械手
人机交互
模拟
接触力
堆积
人机交互
加速度
执行机构
平衡(能力)
控制工程
适应(眼睛)
仿人机器人
康复机器人
作者
Ling Wang,Yu Sun,Yu Sun,Laihao Yang,Yuxuan Sun,Yuxuan Sun,Qingkai Guo,Yixue Liu,Xuefeng Chen,Yajing Shen
出处
期刊:Science Advances
[American Association for the Advancement of Science]
日期:2026-03-04
卷期号:12 (10): eaec3263-eaec3263
标识
DOI:10.1126/sciadv.aec3263
摘要
Achieving human-like forceful manipulation remains a major challenge in robotics because of the lack of critical environmental interaction cues such as collisions, balance, and resistance. We present a torque-angle-pressure (TAP) tactile sensor leveraging magnetic flux density gradients to achieve bidirectional, ultrasensitive (~0.1°, ~0.4 newton-millimeter), and high-linearity ( R 2 = 0.99) sensing over a wide range (±241.6 newton-millimeter) through a single readout channel. The accurate torque sensing ability provides both force and distance information, bringing the environment into the interaction loop. A TAP-equipped robot can perform vision-free stable object placement and complete a balance beam stacking challenge in just 2.4 seconds with a success rate of 81.5%—both measured metrics surpassing human performance. It also supports adaptive daikon slicing with real-time posture and motion adjustments—capabilities rarely achievable in existing robotic systems. This work advances tactile sensing, enables forceful manipulation in unstructured environments, and represents a key step toward effective human-robot collaboration.
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