Sources familiar with the matter revealed that Luo Jianlan, Chief Scientist of AGIBOT, is planning to leave the company to start his own business. He is currently in contact with industry investors, and his startup will focus on embodied intelligence.
To delve deeper into the core field of robotic intelligent control, Luo Jianlan pursued advanced studies abroad, attending the University of California, Berkeley, where he earned a Master’s degree in Computer Science and a PhD in Robotics Control. He studied under renowned robotics scholar Pieter Abbeel, solidifying his theoretical foundation in robot learning and intelligent control. After graduating with his PhD, he did not stop at academic research but instead devoted himself to the forefront of global technology industry, working at Google X and Google DeepMind as a research scientist. He was deeply involved in exploring the industrial applications of reinforcement learning, experiencing firsthand the entire process of artificial intelligence technology from algorithm iteration to practical application, accumulating invaluable practical experience in both academia and industry.
At a critical juncture where the global humanoid robot industry is accelerating its competition and domestically produced AI robots urgently need to overcome core technological bottlenecks, Luo Jianlan chose to return to China and officially join AGIBOT as Chief Scientist, Senior Vice President, and Partner, taking the lead in the research and development of AGIBOT’s embodied intelligence core technologies.
Luo Jianlan’s core work involves building a systematic R&D system, leading the establishment of the AGIBOT Embodied Intelligence Research Center, and comprehensively guiding the development of cutting-edge robot algorithms, technological innovation, and engineering implementation. He predicts that in the next 3-5 years, embodied intelligence will move beyond the laboratory demonstration stage and enter a critical period of large-scale deployment. The industry’s core competitiveness will shift from competing on algorithm parameters to a comprehensive competition based on real-world data accumulation, hardware and software synergy, and engineering implementation efficiency. Only teams that adhere to “real-world deployment and closed-loop iteration” can achieve continuous technological breakthroughs.