Abstract: 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 to 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 a representative state-of-the-art approach in
experimental scenarios, inspired by realistic industrial Human-
Robot Interaction settings.
