Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies

This is the accompanying video of our RSS 2024 paper titled "Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies".

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

Reference:
Mike Allenspach, Michael Pantic, Rik Girod, Lionel Ott, Roland Siegwart; "Task Adaptation in Industrial Human-Robot Interaction: Leveraging Riemannian Motion Policies"; Robotics, Science and Systems (RSS) 2024

Affiliations:
All authors are with the Autonomous Systems Lab, ETH Zurich, 8092 Switzerland.