用强化学习快速搭建人体动作交互原型，训练速度提升98%
MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning
- 通过可视化界面快速配置任务与用户模型
- 分钟级完成肌肉驱动仿真用户训练，最快提速98%
- 适合无专业背景的交互设计师快速上手
基于强化学习的生物力学仿真有望革新人机交互研究与交互设计，但当前存在可用性差、可解释性不足的问题。本文以人类动作周期为设计视角，识别出生物力学强化学习框架的关键局限，并提出 MyoInteract 框架，用于快速原型化生物力学人机交互任务。该框架通过直观的图形界面，使设计者可在数分钟内完成任务设定、用户模型构建与训练参数配置。系统可在数分钟内训练并评估肌肉驱动的模拟用户，训练时间最多缩短98%。对12名交互设计师开展的工作坊研究表明，即使无生物力学强化学习经验的初学者，也能在单次会话中成功完成目标导向动作的设置、训练与评估。本工作将生物力学强化学习从耗时数日的专业任务转变为一小时内的可访问流程，显著降低入门门槛，加速人机交互生物力学研究的迭代周期。
原文摘要 · Abstract (English)
Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.
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