A hierarchical language-conditioned policy that synthesizes an adaptively selected novel view reaches 90.4% single-task success and 2.93 average consecutive-task length on CALVIN, ahead of prior non-foundation-model methods.
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NVSPolicy: Adaptive Novel-View Synthesis for Generalizable Language-Conditioned Policy Learning
A hierarchical language-conditioned policy that synthesizes an adaptively selected novel view reaches 90.4% single-task success and 2.93 average consecutive-task length on CALVIN, ahead of prior non-foundation-model methods.