Agile Robots and Google DeepMind partner to bring intelligence to robotics
/ 3 min read- Strategic partnership to integrate Gemini Robotics foundation models with Agile Robots’ hardware
- The partners will collaborate to advance adaptable and reasoning robots for a broad range of applications
Munich, 24. March 2026: Agile Robots SE, a global leader in intelligent robotics solutions, and Google DeepMind, the world’s leading AI lab, have formed a strategic research partnership to advance next generation AI robotics.
The partnership is built on a belief that applying AI in the physical world will be transformative. The collaboration will bring cutting-edge AI into real-world robotic systems and enable adaptable, reasoning robots for industrial environments. It combines Google DeepMind’s Gemini Robotics foundation models with Agile Robots’ scalable industrial robotics platform.
By bringing together Agile Robots’ hardware and other AI robotic solutions developed in Germany, with Google DeepMind’s Gemini Robotics foundation models, the two teams will improve performance via robot deployment, data collection, model training and iteration.
Zhaopeng Chen, Agile Robots CEO and Founder said: "Agile Robots has already installed over 20,000 robotics solutions worldwide, proving intelligent automation at scale. The huge opportunity ahead lies in autonomous, intelligent production systems that can transform entire industries. Integrating Google DeepMind’s Gemini Robotics models into our robotic solutions positions us at the cutting edge of this rapidly growing market."
Carolina Parada, Senior Director and Head of Robotics, Google DeepMind said: “We are excited to partner with Agile Robots as we develop more advanced AI models for the next generation of robots and to scale their impact across sectors. This research partnership is an important step in bringing the impact of AI to the real world.”
The partnership will first focus on high-value industrial use cases in sectors with acute and growing demand for adaptable, reliable automation. This includes industrial and manufacturing tasks where reliability and scale are critical.
Jointly training, deploying and testing robotic solutions will create a scalable AI flywheel: data from real operations improves the models, and improved models expand robotic capabilities - unlocking broader deployment.