Max Robotics — Global Service Robot Directory + US Certification

About

Devleena Das is a researcher whose work sits at the intersection of explainable artificial intelligence (XAI) and human-robot interaction, with a particular focus on making robotic failures understandable and recoverable by everyday users. Her research addresses a critical challenge in modern robotics: as intelligent systems become increasingly embedded in daily life, their occasional failures must be interpretable not just by trained technicians, but by ordinary people who encounter them first. Das's most influential contribution, "Explainable AI for Robot Failures" (2021), has garnered 89 citations and established her as a leading voice in applying XAI principles to fault recovery in robotic systems. Building on this foundation, she has pioneered semantic-based explanation frameworks that leverage scene graphs and pairwise ranking to generate contextually rich, natural language explanations without requiring extensive hand-annotation. Her more recent work on explainable knowledge graph embeddings extends these ideas into sequential robot decision-making, providing inference reconciliation frameworks that illuminate how learned domain knowledge shapes robot behavior. Across her publication record, Das consistently bridges technical rigor with human-centered design, ensuring that AI transparency translates into practical assistance for non-expert users — a contribution with meaningful implications for the responsible deployment of autonomous systems.