Abstract
Pointing is a key mode of interaction with robots, yet most prior work has focused on recognition rather than generation. We present a motion capture dataset of human pointing gestures covering diverse styles, handedness, and spatial targets. Using reinforcement learning with motion imitation, we train policies that reproduce human-like pointing while maximizing precision. Results show our approach enables context-aware pointing behaviors in simulation, balancing task performance with natural dynamics.
Abstract (translated)
指向是与机器人互动的关键方式，然而大多数先前的研究主要集中在识别而非生成上。我们提出了一套人类指向手势的动作捕捉数据集，涵盖了多样化的风格、左右手使用偏好及空间目标。通过结合运动模仿的强化学习方法，我们训练出了能够再现类人指向动作并最大化精度的策略。实验结果显示，我们的方法能够在模拟环境中实现情境感知的指向行为，在保持任务性能的同时兼顾自然动态表现。
URL
https://arxiv.org/abs/2509.12880
