Multimodal Interaction and Intention Communication for Industrial Robots

TL;DR

This paper explores multimodal communication methods for industrial robots, enhancing human-robot interaction by integrating speech, movement, and gaze cues, and evaluates these approaches through experiments with a small anthropomorphic robot.

Contribution

It introduces a multimodal, LLM-enhanced communication framework for non-humanoid industrial robots and demonstrates its effectiveness through experimental evaluation.

Findings

Users perceive the robot as more expressive and engaging.

The framework improves task understanding and communication clarity.

Experimental results validate the approach's potential for industrial settings.

Abstract

Successful adoption of industrial robots will strongly depend on their ability to safely and efficiently operate in human environments, engage in natural communication, understand their users, and express intentions intuitively while avoiding unnecessary distractions. To achieve this advanced level of Human-Robot Interaction (HRI), robots need to acquire and incorporate knowledge of their users' tasks and environment and adopt multimodal communication approaches with expressive cues that combine speech, movement, gazes, and other modalities. This paper presents several methods to design, enhance, and evaluate expressive HRI systems for non-humanoid industrial robots. We present the concept of a small anthropomorphic robot communicating as a proxy for its non-humanoid host, such as a forklift. We developed a multimodal and LLM-enhanced communication framework for this robot and evaluated it in several lab experiments, using gaze tracking and motion capture to quantify how users perceive the robot and measure the task progress.