Researchers have proposed a unified, architecture-agnostic probabilistic formulation for embodied foundation models, which are used in robot learning and control. This new formulation leverages stochastic kernels to characterize these models through conditional output distributions and action-realization kernels, enabling the induction of task-conditioned robot policies. The approach connects pretraining and post-training phases to policy deployment, accommodating various model-specific objectives and training strategies. The formulation has been demonstrated with existing models like OpenVLA and DreamZero, covering different action generation methods. AI
IMPACT This formulation could streamline the development and deployment of more versatile and capable embodied AI systems for robotics.
RANK_REASON The cluster contains an academic paper detailing a new formulation for embodied foundation models. [lever_c_demoted from research: ic=1 ai=1.0]
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