提升人机交互中生物力学模型的拟真度，让数字人体更精准操作触屏。
Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI
- 用课程学习与动作掩码等训练技巧，优化强化学习过程。
- 在触屏指针任务中显著提升动作精度，超越现有方法。
- 适合做高保真人机交互模拟的研究者参考。
生物力学前向仿真在人机交互中潜力巨大，可生成类人运动。但通过强化学习训练生物力学模型仍具挑战性，尤其对手机触屏所需的精细、灵巧动作。现有方法交互拟真度有限，需简化模型且泛化能力差。本文提出实用训练改进方案：课程学习、动作掩码、复杂网络结构及环境微调，显著缩短训练时间，提升触屏操作准确性，支持更复杂的生物力学模型。实验以触屏指针任务验证，结果表明该方法在交互拟真度上优于现有方法。为HCI研究者提供了可落地的建模训练指南。
原文摘要 · Abstract (English)
Biomechanical forward simulation holds great potential for HCI, enabling the generation of human-like movements in interactive tasks. However, training biomechanical models with reinforcement learning is challenging, particularly for precise and dexterous movements like those required for touchscreen interactions on mobile devices. Current approaches are limited in their interaction fidelity, require restricting the underlying biomechanical model to reduce complexity, and do not generalize well. In this work, we propose practical improvements to training routines that reduce training time, increase interaction fidelity beyond existing methods, and enable the use of more complex biomechanical models. Using a touchscreen pointing task, we demonstrate that curriculum learning, action masking, more complex network configurations, and simple adjustments to the simulation environment can significantly improve the agent's ability to learn accurate touch behavior. Our work provides HCI researchers with practical tips and training routines for developing better biomechanical models of human-like interaction fidelity.
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