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This work introduces InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences.
Skel-WAM is introduced, a world action model that bridges differences between human and robot motion through a unified hand-skeleton motion interface, and demonstrates that a shared skeletal interface enables joint learning across human and robot data and expands robot task coverage through complementary human demonstr...
EgoAfford is introduced, a benchmark designed to connect the semantic roles of participating objects with task-state-aligned visual observations and multi-step planning and EgoLens, a 3B multimodal large language model with role-specific mask decoders, as an in-domain reference model for this joint task.
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