机器人中的世界模型挑战与机遇

Language had a strange advantage robotics does not: Text is already a compressed, shared interface for human thought, while physical action is split across bodies, sensors, surfaces, speeds, and failure modes. $5B + is already betting on world models, $18B has gone into robotics, and yet the field still has no widely trusted shared benchmark, no architecture convergence, and a 100,000-year data gap between robot experience and the data scale behind modern AI. World models are promising because they try to predict what will happen before a robot acts, but prediction alone does not solve data collection, evaluation, real-time control, or deployment reliability. The serious startup opportunities sit in those bottlenecks. Whoever builds the data loops, eval systems, memory layers, inference stack, or vertical deployment engines may shape embodied AI more than the teams arguing over model labels today A great piece from Charlotte Xia (@xia_char)  > **引用原帖 Charlotte Xia (@xia_char):** > Jim Fan's "Great Parallel" thesis: embodied AI will scale like LLMs did. > $5B+ is already betting on #worldmodels. $18B into #robotics. But the field has no shared benchmark, no convergence on architecture, and a 100,000-year data gap (per @Ken_Goldberg). > In this blog, Matt and I wrote down some thoughts on why we think the Great Parallel may be harder to come true than it seems, and where we believe the real startup opportunities in the space. > https://t.co/NKs12Lw3rD > https://x.com/xia_char/status/2066899628055023616 Rohan Paul (@rohanpaul_ai): LLMs improved through pre-training, supervised fine-tuning, and reasoning RL, while robots might improve through world modeling, action fine-tuning, and physical RL. The blog argues that this parallel is attractive, but robotics still lacks the shared architecture, cheap data, and trusted benchmarks that made the LLM path work. https://t.co/jI58icx0Vp