Helen Beierling

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About

Helen Beierling is a leading researcher at the intersection of human-robot interaction, explainable AI, and transparent autonomous systems. Her work focuses on bridging the gap between complex robotic behavior and lay user understanding, with a particular emphasis on how non-expert users can effectively teach, trust, and collaborate with robots in everyday settings. Beierling’s most cited paper, “What you need to know about a learning robot: Identifying the enabling architecture of complex systems” (2024, 5 citations), establishes a foundational framework for making robot decision-making processes accessible to users, especially during error scenarios. Her follow-up work, “Technical Transparency for Robot Navigation Through AR Visualizations” (2023, 3 citations), pioneers the use of augmented reality to demystify robot navigation, directly addressing the trust barrier encapsulated in the adage “you don’t trust things you don’t understand.” Most recently, her 2025 study “The power of combined modalities in interactive robot learning” explores how integrating multiple communication channels can empower lay users to teach robots more intuitively. Beierling’s contributions are critical as robots enter homes and care facilities, ensuring these technologies remain comprehensible, trustworthy, and usable for everyone.