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BeJEPA: A Goal-Conditioned Humanoid Behavior World Model via Unsupervised Reinforcement Learning

The paper proposes BeJEPA, a goal-conditioned humanoid behavior world model learned through unsupervised reinforcement learning from human motion priors and robot interaction, using a joint-embedding predictive architecture (JEPA) to learn a shared representation of body configuration and world placement.

Part of the event: BeJEPA: Goal-Conditioned Humanoid Behavior World Model Paper Released

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