Rethinking Representations for World-Action Modeling
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要旨より
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design of this space through controlled comparisons, finding that neither reconstruction fidelity nor pre-trained perceptual features alone ensure effective policy learning.
要旨より。当社による要約は作成中です。