Max Robotics — Global Service Robot Directory + US Certification

About

Daniel Morton is an emerging researcher at the forefront of robot learning and embodied artificial intelligence, with work that addresses one of robotics' most pressing challenges: building generalizable manipulation policies from diverse, large-scale data. His most influential contributions center on the construction and utilization of expansive robot learning datasets and the development of scalable foundation models for robotic control. Morton's involvement in the **Open X-Embodiment** project (2024, 119 citations) represents a landmark collaborative effort to consolidate robotic learning datasets across multiple platforms, enabling large, high-capacity models—including the RT-X family—to transfer knowledge efficiently across downstream tasks, mirroring breakthroughs seen in NLP and computer vision. Complementing this, his work on **DROID** (2024, 108 citations) tackles the logistical and methodological difficulties of collecting high-quality, in-the-wild robot manipulation data at scale, producing one of the field's most comprehensive manipulation benchmarks to date. Together, these contributions have amassed over 230 citations in a single year, reflecting their rapid and significant uptake by the research community. Morton's work is helping lay the data infrastructure groundwork necessary for the next generation of robust, generalizable robotic systems.