  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.
