InternW0: A Foundational Physical World Model for Efficient Real-World Interactions
Shanghai AI Laboratory introduces InternW0, a physical world model that jointly predicts visuals and continuous robot control.
InternW0 is the first model in Shanghai AI Laboratory's InternW physical world model series, combining omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations. A high-capacity video expert and a faster lightweight action expert jointly learn visual dynamics and continuous robot control with flow matching, reusing layerwise key-value context for new observations. Training uses about 7,200 hours of heterogeneous robot and egocentric data, including the 275-hour EgoLab laboratory set. Evaluations cover simulation plus a 15-stage metal-organic framework synthesis and 5-stage contact- and force-aware pipetting.
- Asymmetric video-action architecture uses flow matching
- Reuses layerwise key-value context instead of regenerating futures
- Trained on about 7,200 hours, including 275-hour EgoLab
- Evaluated on MOF synthesis and force-aware pipetting
- Organizations
- Shanghai AI Laboratory
- Countries
- China
- AI models
- InternW0
From the original article
1 min read at huggingface.co
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By Jisong Cai, Yao Mu, Ganlin Yang · huggingface.co
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