In real-world industrial environments, modern robots often rely on human operators for crucial decision-making and mission synthesis from individual tasks. Effective and safe collaboration between humans and robots requires systems that can adjust their motion based on human intentions, enabling dynamic task planning and adaptation. Addressing the needs of industrial applications, we propose a motion control framework that (i) removes the need for manual control of the robot's movement; (ii) facilitates the formulation and combination of complex tasks; and (iii) allows the seamless integration of human intent recognition and robot motion planning. For this purpose, we leverage a modular and purely reactive approach for task parametrization and motion generation, embodied by Riemannian Motion Policies. The effectiveness of our method is demonstrated, evaluated, and compared to \remove{state-of-the-art approaches}\add{a representative state-of-the-art approach} in experimental scenarios inspired by realistic industrial Human-Robot Interaction settings.

翻译：在现实工业环境中，现代机器人常依赖人类操作员进行关键决策，并将独立任务合成为完整任务。要实现人机间有效且安全的协作，系统需能根据人类意图调整自身运动，从而实现动态任务规划与适应。针对工业应用需求，我们提出一种运动控制框架，该框架（i）无需人工操控机器人运动；（ii）便于复杂任务的表述与组合；（iii）支持人类意图识别与机器人运动规划的无缝集成。为此，我们采用模块化纯反应式方法进行任务参数化与运动生成，该方法以黎曼运动策略为核心实现。我们在模拟真实工业人机交互场景的实验环境中，对本方法的有效性进行了验证、评估，并与\remove{现有先进方法}\add{一种代表性先进方法}进行了对比。