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StarSeaTech Advances Embodied Intelligence at World Robotics Conference 2026
Original from leaderobot.com: From Models to Productivity: Exploring the Next Steps in Embodied Intelligence

StarSeaTech Advances Embodied Intelligence at World Robotics Conference 2026

During the 2026 World Robotics Conference, StarSeaTech hosted a forum titled 'From Models to Productivity' at the Beiren Yichuang Exhibition Center. The event gathered experts from academia, developers, and corporate executives to discuss innovations in embodied intelligence models, machine evolution, commercialization, and technological boundaries, showcasing the latest thoughts on technology iteration and industrial application.

StarSeaTech's Chief Scientist Zhao Xing highlighted the evolution of embodied intelligence technology through instinctive, operational, and evolutionary intelligence levels. The industry is transitioning from isolated technological breakthroughs to systemic competition involving models, data, entities, infrastructure, and applications. StarSeaTech has established a comprehensive productivity flywheel for embodied intelligence, focusing on real machine data, model iteration, and physical task execution supported by infrastructure for continuous optimization.

Key announcements included the upgrade of the G0.5 foundational model, the introduction of the Fast-WAM world model pre-training plan, and the launch of the G-Fleet distributed reinforcement learning system. These developments aim to enhance model capabilities, speed up inference times, and support continuous learning in real robot clusters. No further timeline was disclosed at the time of publication.

Editor's Note

The advancements in embodied intelligence presented by StarSeaTech at the World Robotics Conference reflect a significant shift towards integrating AI with physical robotics. As industries seek to enhance productivity through intelligent automation, the focus on foundational models and real-world applications will be crucial for future developments in this sector.

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