Max Robotics — Global Service Robot Directory + US Certification

About

David Yunis is a robotics and artificial intelligence researcher whose work spans embodied reasoning, autonomous navigation, and robot design optimization. He is perhaps best known for his development of **Statler**, a state-maintaining language model framework that advances how intelligent robots reason about their environments — moving beyond action-history tracking to incorporate dynamic world-state representations. This work has garnered over 30 citations across its 2023 and 2024 publications, reflecting its significant resonance within the robotics and large language model communities. Yunis has also made meaningful contributions to robot localization, co-developing optimization techniques for beacon-based positioning systems that jointly refine both beacon placement and inference accuracy — critical for autonomous robots operating in GPS-denied environments. His 2019 work on co-optimizing robot morphology and control policy through deep reinforcement learning further demonstrates his breadth, tackling the fundamental challenge of designing robots and their behaviors in tandem rather than in isolation. Across his research portfolio, Yunis consistently addresses the intersection of learning, reasoning, and physical autonomy, making him a versatile contributor to modern robotics. Students interested in embodied AI, localization, or learned robot design will find his work both technically rigorous and highly relevant to contemporary challenges in the field.