国内第一视角数据最早押注者，北大卢宗青：隐空间才是具身的路
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Merged summary
TL;DR - BeingBeyond launched Being-H0.8, described as the first latent world-action model to jointly encode vision, touch, actions, and future state changes for robot control. Its latent-space approach targets real-time deployment at roughly 1% of pixel-based video-model training cost.
- Being-H0.8 adds tactile signals to large-scale pretraining, aiming to model physical interactions rather than merely visual observations.
- The company has curated over 500,000 hours of first-person human video, arguing it offers greater scale and diversity than robot-collected or simulated data.
- Its models predict actions and world responses directly in embedding space, avoiding costly frame generation and supporting faster inference.
- Founder Lu Zongqing cautions that embodied AI still lacks a proven paradigm comparable to next-token prediction for LLMs; even world models may not be the final answer.
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国内第一视角数据最早押注者，北大卢宗青：隐空间才是具身的路
Public signals
N/A
TL;DR - BeingBeyond launched Being-H0.8, described as the first latent world-action model to jointly encode vision, touch, actions, and future state changes for robot control. Its latent-space approach targets real-time deployment at roughly 1% of pixel-based video-model training cost.
- Being-H0.8 adds tactile signals to large-scale pretraining, aiming to model physical interactions rather than merely visual observations.
- The company has curated over 500,000 hours of first-person human video, arguing it offers greater scale and diversity than robot-collected or simulated data.
- Its models predict actions and world responses directly in embedding space, avoiding costly frame generation and supporting faster inference.
- Founder Lu Zongqing cautions that embodied AI still lacks a proven paradigm comparable to next-token prediction for LLMs; even world models may not be the final answer.