Max Robotics — Global Service Robot Directory + US Certification

About

Eric Sample is a researcher whose work sits at the intersection of robotics, probabilistic reasoning, and human-robot collaboration. His most influential contributions center on Bayesian data fusion techniques that enable robotic systems to meaningfully incorporate ambiguous, human-generated information alongside conventional sensor data. His 2012 paper "Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration," which has garnered 64 citations, established a foundational framework for modeling so-called "soft" categorical information through hybrid continuous-to-discrete likelihoods — a significant advance in making robots more effective collaborative partners. That same year, he contributed an experimental validation of these methods in real robotic systems, further grounding the theoretical work in practical application. Sample has also explored decentralized, information-rich planning frameworks for multi-agent missions involving both autonomous vehicles and human operators, addressing scalability challenges in complex search-and-tracking scenarios. More recently, his 2021 work on perception robustness testing demonstrates a continued commitment to ensuring the safety and reliability of deployed robotic systems across varied real-world conditions. Taken together, his research reflects a sustained effort to build trustworthy, human-aware autonomous systems capable of operating intelligently in uncertain environments.