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.

翻译：基于强化学习（RL）的生物力学仿真具有革新人机交互（HCI）研究与交互设计的潜力，但目前其可用性与可解释性不足。本研究以“人类行动周期”为设计视角，剖析了现有生物力学强化学习框架的关键局限，并开发了MyoInteract——一个用于生物力学人机交互任务快速原型开发的新型框架。MyoInteract允许设计者通过易于使用的图形界面在数分钟内完成任务设置、用户模型构建及训练参数配置。该框架能在数分钟内完成肌肉驱动仿真用户的训练与评估，将训练时间缩短最高达98%。一项包含12位交互设计师的研讨研究表明，MyoInteract使得生物力学强化学习新手能在单次会话中成功设置、训练并评估目标导向的用户动作。本工作将生物力学强化学习从耗时数日的专家级任务转变为可在一小时内完成的可及工作流程，显著降低了人机交互生物力学研究的入门门槛，并加速了其迭代周期。