让机器人更懂用户动作，精准定位交互关键部位。
TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction
- 用视觉语言模型理解指令，聚焦任务相关身体部位
- 在真实交互场景中，关键部位定位误差降低37%
- 适合需要自然人机交互的机器人系统研发
精准的3D人体关键点定位是实现机器人与用户自然安全物理交互的关键技术。传统方法主要关注全身相对于根关节的重建质量，但在实际人机交互（HRI）场景中，机器人更关心在第一人称视角3D坐标系下任务相关身体部位的精确度量空间定位。本文提出TAIHRI，首个专为近距离人机交互感知设计的视觉语言模型，能够理解用户运动指令，并引导机器人注意力到最相关的关键点。通过将3D关键点量化至有限交互空间，TAIHRI利用2D关键点推理与下一步词预测，精准定位关键身体部位的3D空间坐标，并无缝适配自然语言控制或全局空间人体网格恢复等下游任务。在第一人称交互基准测试中，TAIHRI在任务关键部位的估计精度显著优于现有方法。代码已开源：https://github.com/Tencent/TAIHRI。
原文摘要 · Abstract (English)
Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users' motion commands and directing the robot's attention to the most task-relevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.
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