Agibot Chief Scientist Rejects LLM Path for Robotics

Industry context: For AI and robotics practitioners, progress in embodied systems depends less on simply scaling model size and more on building integrated, deployable data loops and shared standards. Reporting by Kr-Asia describes Luo Jianlan, chief scientist at Agibot and an associate professor at the Shanghai Innovation Institute, arguing that embodied intelligence cannot simply copy the development path of large language models (LLMs). Kr-Asia reports Luo warned that many so-called embodied foundation models are closer to mid-training or fine-tuning because high-quality, multi-scenario robot interaction data, including failures and long-tail events, remains scarce. The article traces a recent shift in China's robotics scene away from body-design fixation toward system-level concerns: data, models, infrastructure, and the ability for those elements to reinforce one another in real-world deployment, per Kr-Asia.