用强化学习生成自然且精准的人类指向动作
Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
- 通过动作模仿强化学习训练指向策略
- 在仿真中实现高精度与自然动态的平衡
- 适合研究人机交互与机器人动作生成者
指向是人机交互的重要方式，但以往研究多聚焦于识别而非生成。本文构建了一个涵盖多种风格、手性及空间目标的人类指向动作捕捉数据集。基于强化学习与动作模仿，训练出能复现人类指向行为并最大化精确度的策略。实验表明，该方法在仿真中实现了上下文感知的指向行为，兼顾任务性能与自然动力学特征。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。