Max Robotics — Global Service Robot Directory + US Certification

About

Maximilian Diehl is a robotics researcher whose work sits at the intersection of explainable artificial intelligence, human-robot interaction, and automated planning. His research focuses on enabling robots to operate transparently and reliably in human-centered environments — a challenge that demands both technical sophistication and a deep understanding of human trust. Diehl's most influential contributions center on causal reasoning for robotic systems. His 2022 letter on causal-based failure explanation (35 citations) introduced a novel method allowing robots to identify and communicate why they failed, directly addressing the transparency gap in human-robot collaboration. This work was extended in a 2023 paper (23 citations) that enables robots to predict and actively prevent failures before they occur. Together, these studies establish a compelling framework for self-aware, adaptive robots. Equally notable is his 2021 work on automated generation of robotic planning domains (32 citations), which removes the bottleneck of manual domain engineering by learning action structures from observation. His earlier research on Augmented Reality interfaces for robot learning verification (16 citations) further demonstrates a consistent commitment to making robot decision-making interpretable and accessible to human collaborators — a theme that ties his entire research agenda together.