仅用关节传感器实现机器人全身触觉定位，无需额外硬件。
UniTac: Whole-Robot Touch Sensing Without Tactile Sensors
- 利用关节传感器数据，通过学习模型推断触觉位置。
- 在Franka上定位误差≤8.0厘米，Spot上≤7.2厘米，频率达2000Hz。
- 适合希望低成本添加触觉能力的机器人研究者使用。
机器人通过触觉感知可更好地与人类及非结构化环境交互。然而，大多数商用机器人未配备触觉皮肤，难以实现如接触定位等基本触觉功能。我们提出UniTac，一种仅依赖本体感觉关节传感器、无需额外传感器的全身体触觉感知方法。该方法使仅配备关节传感器的机器人具备接触定位能力。目标是推动触觉感知普及，为人机交互（HRI）研究者提供即插即用的触觉感知工具。我们在Franka机械臂和Spot四足机器人上验证了该方法：在Franka上定位精度达8.0厘米以内，在Spot上定位精度达7.2厘米以内，采样频率约2000 Hz，运行于RTX 3090 GPU，且未对机器人添加任何新传感器。项目网站：https://ivl.cs.brown.edu/research/unitac。
原文摘要 · Abstract (English)
Robots can better interact with humans and unstructured environments through touch sensing. However, most commercial robots are not equipped with tactile skins, making it challenging to achieve even basic touch-sensing functions, such as contact localization. We present UniTac, a data-driven whole-body touch-sensing approach that uses only proprioceptive joint sensors and does not require the installation of additional sensors. Our approach enables a robot equipped solely with joint sensors to localize contacts. Our goal is to democratize touch sensing and provide an off-the-shelf tool for HRI researchers to provide their robots with touch-sensing capabilities. We validate our approach on two platforms: the Franka robot arm and the Spot quadruped. On Franka, we can localize contact to within 8.0 centimeters, and on Spot, we can localize to within 7.2 centimeters at around 2,000 Hz on an RTX 3090 GPU without adding any additional sensors to the robot. Project website: https://ivl.cs.brown.edu/research/unitac.
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