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

TL;DR

This paper introduces a Riemannian Motion Policies-based framework for adaptive, safe, and efficient human-robot collaboration in industrial settings, enabling dynamic task adaptation and human intent integration.

Contribution

It presents a modular, reactive motion control system that removes manual control, simplifies complex task formulation, and seamlessly incorporates human intent recognition.

Findings

Demonstrated effectiveness in realistic industrial scenarios

Compared favorably to state-of-the-art approaches

Enables dynamic task adaptation and human-robot collaboration

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 \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.