Projection Mapping Implementation: Enabling Direct Externalization of Perception Results and Action Intent to Improve Robot Explainability

TL;DR

This paper presents a projection mapping implementation that directly visualizes a robot's perception and action states onto its environment, enhancing transparency and interpretability in human-robot interactions.

Contribution

It introduces a novel projection mapping tool for robotics, filling a gap in existing visualization methods and providing practical code and documentation for implementation.

Findings

Enables direct visualization of robot internal states.

Improves robot explainability and human understanding.

Provides accessible tools and resources for the robotics community.

Abstract

Existing research on non-verbal cues, e.g., eye gaze or arm movement, may not accurately present a robot's internal states such as perception results and action intent. Projecting the states directly onto a robot's operating environment has the advantages of being direct, accurate, and more salient, eliminating mental inference about the robot's intention. However, there is a lack of tools for projection mapping in robotics, compared to established motion planning libraries (e.g., MoveIt). In this paper, we detail the implementation of projection mapping to enable researchers and practitioners to push the boundaries for better interaction between robots and humans. We also provide practical documentation and code for a sample manipulation projection mapping on GitHub: https://github.com/uml-robotics/projection_mapping.