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Purdue University
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DREAM-Chunk transforms action chunking by leveraging latent world models to enhance robustness against stochastic dynamics without the need for policy retraining.
Achieve high-fidelity 4D mesh generation from video by cleverly repurposing existing positional encodings for temporal information, sidestepping the usual trade-offs between expressiveness and pretraining compatibility.
Trade strict equivariance for a performance boost: this weight-space projection method lets you dial in the right amount of symmetry for your task, even on ImageNet.
